Personalized Coupon Campaigns:

Understanding and Directing Customers

1

Alicia Baik2

July 7, 2015

1I am grateful to my advisors Simon Anderson and Federico Ciliberto for their guid-ance. This work has benefited from conversations with Karim Chalak, Kenneth Elzinga,Maxim Engers, Leora Friedberg, Nathan Larson, Charles Murry, Denis Nekipelov, WilliamScherer, Steven Stern, Rajkumar Venkatesan, and comments from Virginia and various in-dustry professionals. The author thanks a national retailer consortium for sharing data. Iacknowledge financial support from the Bankard Fund for Political Economy. I am solelyresponsible for any errors.

2Department of Economics, University of Virginia P.O. Box 400182, Charlottesville, VA22904-4182, USA. ab3ja@virginia.edu.

Abstract

Firms use two types of targeted coupons: Personalized rewards and targeted promo-

tions. Personalized rewards show appreciation to loyal customers by surprising and

delighting them with a discount for a broad set of frequently purchased products. Tar-

geted promotions, on the other hand, recommend and o↵er discounts for a specific set

of products relevant to a targeted customer group. I find that while the redemption

rates are higher for personalized rewards, the spillover e↵ects of targeted promotions

imply higher overall sales for targeted promotions. Targeted promotions have increas-

ing returns to successive campaigns by increasing probability of coming to the store

and spending once in the store while personalized rewards have decreasing returns.

Further, I identify department sales of the promoted products as the primary spillover

channel through which targeted promotions increase sales once customers are in the

store.

JEL Classifications: L21, M31, M37

Keywords: Consumer Targeting; Advertising; Spillover E↵ects; Marketing; Empir-

ical Industrial Organization

1 Introduction

Technological improvements enable firms to observe behavioral characteristics of cus-

tomers and target recommendations and discounts relevant to a customers’ revealed

preferences. Targeted advertisements have emerged as a promising way for retailers

to more precisely interact with their customers, decreasing waste relative to mass

marketing campaigns. By focusing on products relevant to customers’ tastes, mar-

keters have shown that targeted advertisements elicit higher response rates than non-

targeted advertisements.

When assessing the e↵ectiveness of targeted advertisements, the primary metric

used is the consumer response to the targeted product, either through the purchase of

the product or the click through rate of the online advertisement. While this metric is

important for understanding the ROI of an advertisement, it misses a related metric

which may tell a more complete story. For retailers with multiple products, the

spillover e↵ect of the targeted advertisement may have a more significant impact on

store sales than the purchase of the targeted product; spillover e↵ect refers to the

change in spending of related products not directly included targeted advertisements.

With a unique dataset from a single unidentified national retailer, I am able to

analyze both the direct and indirect spillover e↵ects of targeted advertisements for

two targeting strategies: personalized reward and targeted promotions. Personalized

rewards show appreciation to loyal customers by surprising and delighting them with

a discount for a broad set of frequently purchased products. Since the products

are personalized, they have a higher redemption rate and serve to cut the cost of

shopping at the store. Targeted promotions, on the other hand, recommend and o↵er

discounts for a specific set of products relevant to a targeted customer group. The

same discounts are given to a group of customers selected for a campaign.

The data contains the complete purchase history of 2, 500 households in 369 store

1

locations over a two year period. A subset of households receives one or both of each

type of targeted coupon campaign. Using the retailer’s categorization of campaign

type, I then label the types as either personalized reward or targeted promotion

based on observed characteristics of the campaigns and descriptions of the retailer’s

campaign types.

Roughly half-way through the sample period, the retailer began sending personal-

ized reward and targeted promotion coupon campaigns to a subset of its customers.

The first weeks of the sample serve as a control period during which no household

receives any targeted campaigns. While both campaign types are primarily sent to

households exhibiting loyalty through frequent visits or high weekly sales, the re-

tailer intentionally uses test and control groups within loyalty segments to assess the

e↵ectiveness of the campaigns.

The purpose of this paper is to assess the di↵erential impact each type of targeted

campaign has on household weekly sales during the weeks of the campaigns. First, in

order to determine the overall e↵ect each campaign has on expected sales, I regress

weekly sales on the receipt of each type of campaign by week of campaign. I find

that while both campaigns increase expected sales, on average targeted promotions

outperform personalized rewards in increasing sales during campaign weeks.

During the test period, some households receive multiple campaigns of each type. I

test the marginal e↵ect of iterative campaigns by regressing weekly sales on the receipt

of successive campaigns by type (testing the e↵ectiveness of the first, second, third

and so on campaign). I find that personalized reward campaigns have diminishing

returns with additional campaigns, suggesting that reward campaigns may be relying

on a short-term surprise and delight e↵ect that loses e↵ectiveness as the household

receives more campaigns. This also suggests that this campaign is not relying on

gratitude to spur sales, at least during the weeks of the campaigns. I also find that

targeted promotion campaigns have increasing returns. Since redemption rates are

2

much lower for targeted promotion campaigns than personalized reward campaigns,

this suggests that targeted promotion campaigns drive weekly sales through indirect

means.

In order to test the indirect spillover e↵ect, I focus on sales conditional on coming

to the store using a method that corrects for selection. I first account for selection

of households that come to the store in a given week and then estimate the sales

within the store, including factors such as redemption of coupons and other discounts.

I find that redemptions are correlated with higher sales for personalized rewards

than targeted promotions. However, outside of the narrow cases when households do

redeem, personalized rewards have no spillover e↵ects on spending at the store while

targeted promotions induce extra spending. Both coupons also encourage higher

likelihood of visiting the store in the weeks of the campaigns.

Finally, I hypothesize that the spillover e↵ect is concentrated in the promoted de-

partments. I am only able to test this for the targeted promotion coupons due to data

limitations in personalized rewards. However, I am able to control for redemptions

for both types and other department discounts. Using Heckit again for department

sales, I find that conditional on visiting the store, households have spillover spending

in the departments of the targeted promotion coupons. That is, even though if they

do not redeem the products in the coupons, they are more likely to buy products in

the surrounding department while on their shopping trip.

Both targeting techniques utilize individual purchase histories to ensure coupons

are relevant to the recipient. Additionally, both campaigns leverage price discounts

as the advertisement vehicle. However, the distinct focus of each campaign leads

to di↵erent outcomes. Because personalized rewards leverage a positive feeling of

surprisingly being rewarded for previous loyalty, the key dimension of interest is the

surprise aspect. As customers become used to receiving discounts, the positive e↵ect

on sales diminish. Additionally, as a reward mechanism, the campaign does not direct

3

purchase behavior of the customer. Thus, there is minimal spillover e↵ect with this

campaign type.

The purpose of targeted promotions is to redirect customer attention to prod-

ucts less frequently purchased by signaling their relevance by simultaneously o↵ering

discounts for products frequently purchased. Because targeted promotions are in-

formative, their impact is persistent and does not rely on surprising customers with

bigger and better. Additionally, the informative recommendations direct customers

to segments of the store the customer is unlikely to visit. Even if the customer does

not redeem the promoted product, the customer is more likely to purchase products

in the promoted department.

This paper will proceed as follows: Section 2 will outline relevant literature; Sec-

tion 3 will describe the grocery industry; Section 4 will outline the grocery choice

conceptual framework; Section 5 will describe the data; Section 6 will outline my em-

pirical strategy for identifying the e↵ect of each targeted coupon on sales, controlling

for coupon receipt; Section 7 will outline my results; and Section 8 concludes.

2 Literature Review

Targeted advertisement research has primarily focused on estimating direct response

(Arora et al., 2008). Multiple studies have shown that advertisements matched to

customer preferences are more e↵ective in engaging response and increasing sales of

the advertised product. Targeted advertisements not only increase e↵ectiveness, they

also decrease cost by reducing wasted advertisements (Iyer et al. 2005). As shopping

moves online, stores are increasingly tailoring their communication with customers.

Many studies show that personalization of emails and websites each increase customer

engagement (Ansari, A. and C. Mela, 2003 and Hauser, J. R., G. L. Urban, G. Liberali,

and M. Braun, 2009).

4

Increased targeting is not always correlated with increased response. Lambrecht

and Tucker (2013) show that dynamic retargeted advertisements are generally less ef-

fective than generic retargeted advertisements unless customers have spent more time

developing their preferences. Tucker (2011) also finds that while matching ad con-

tent to the website and personalizing advertisement content independently increases

e↵ectiveness, combining these strategies decrease engagement.

While it is important to understand the direct impact of targeted advertisements,

my research shows that a related metric tells a more complete story. The spillover

e↵ect of targeted advertisements can drive more sales than the direct e↵ect of the

targeted advertisement, particularly for retailers with multiple products. Marketing

spillover e↵ects have traditionally been analyzed primarily in the context of brand

alliances. It is well documented that when brands have an alliance, customers eval-

uations of brand A will spillover to their evaluations of brand B (Balachander and

Ghose 2003; Baumgarth 2004; Desai and Keller 2002; Janiszewski and Van Osselaer

2000; Park, Jun, and Shocker 1996; Samu, Krishnan, and Smith 1999; Simonin and

Ruth 1998; Vaidyanathan and Aggarwal 2000). Unfortunately, the direction of this

impact has not been consistent. Some studies have found that the spillover e↵ect is

positive (Simonin and Ruth 1998; Washburn, Till, and Priluch 2000), while others

found it was negative (Keller and Aaker 1992; Loken and John 1993; Till and Shimp

1998). Tseng (2010) and Raghubir (2004) both found a negative spillover a↵ect asso-

ciated with gift promotions. Customers tend to discount the gifts’ value, which leads

them to discount the value of the product category with the gift. The mechanism of

spillover e↵ects is assessed in Erdem and Sun (2002), who find that advertisements

reduce the uncertainty of related brands.

As marketers increase their attention to and assessment of multi-channel advertise-

ments, researchers have begun to analyze the spillover e↵ects across channels. Rutz,

O. J. and R. E. Bucklin (2011) find that asymmetric spillover e↵ects with generic

5

and branded search activity. While generic search activity positively a↵ects branded

search activity via increased awareness, branded search does not a↵ect generic search.

Joo et al. (2014) examine the impact television advertisements have on online search

behavior. They find that TV ads lead to increased related online search and increased

branded searches.

Dias et al. (2008) and Venkatesan and Farris (2012) separately identify spillover

e↵ects of targeted advertisements in grocery stores. Dias et al. (2008) finds that when

an online grocery store recommends products based on customers’ purchase history,

sales of recommended items and their categories increase. Additionally, Venkatesan

and Farris (2012) use the same data as this paper to identify what they term an

exposure e↵ect to customized coupons. They distinguish this indirect spillover e↵ect

on propensity to come and sales with the direct redemption e↵ect. I extend the litera-

ture of the spillover e↵ects of targeted advertisements in grocery stores by identifying

increasing spillover returns to targeted promotions and decreasing spillover returns

to personalized rewards. I also show that the primary channel through which the

in-store spillover is realized is in department of the promoted products. Finally, I use

machine learning methods to control for targeting endogeneity to ensure estimated

e↵ects are not biased.

While generally understood to be a price discrimination tool to attract customers

by lowering price (Narasimhan,1984; Bester and Petrakis, 1996; Anderson, Baik, and

Larson, 2015), traditional coupons have also been found to have spillover e↵ects by

increasing sales while customers are in the store and by inducing repurchase of the

advertised product. Heilman, Nakamoto, and Rao (2002) show that in-store surprise

coupons increase sales by increasing unplanned purchases. Nevo and Wolfram (2002)

also find evidence that coupons can induce repurchase of products. This also relates

to an extensive body of research on in-store marketing (SK Hui, Y Huang, J Suher,

JJ Inman, 2005; Breugelmans and Campo, 2011; Inman et al., 2009; Hui et al., 2013;

6

and Chandon et al., 2009).

This paper also contributes to the growing literature of the impact targeted ad-

vertisements have on consumer preference development. Simonson (2005) finds that

a consumer’s stage of preference development may significantly a↵ect the e↵ective-

ness of personalized ad content. In particular, advertisements that convey high-level

characteristics are more e↵ective when customer have a broad idea of what they want

while advertisements that focus on specific products are more e↵ective when con-

sumers have narrowly construed preferences. Lambrecht and Tucker (2013) use this

framework to test how targeting response varies depending on the customer’s state.

Fong (2012) examines this connection by looking at how targeting a↵ects the state of

the customer. He finds that consumer search decreases when consumers receive tar-

geted o↵ers. This paper contributes by identifying the di↵erential impact personalized

rewards have on inducing spillover sales relative to targeted promotions. Targeted

promotions increase awareness of relevant products and increase the consideration

set of the consumer in the department while personalized rewards do not exhibit this

e↵ect.

3 Industry Description

3.1 Targeted Advertising and Consumer Analytics

With the acceleration of the online and mobile platforms and the tracking tools asso-

ciated with it, personalized advertisements have gained a lot of attention. Particular

focus has been placed on tracking user behavior in order to more appropriately place

advertisements to households with relevant tastes for a brand. Figure 1 summarizes

important tracking tools used by marketers personalizing advertisements.

Loyalty analytics firms have been front-runners in tracking consumer behavior

and using this data to develop profitable consumer insights (Ne↵ 2013). Access to

7

Figure 1: Targeting Overview

rich transaction data collected companies enable consumer analytics firms to observe

individual behavioral patterns over time and predict future behavior of their cus-

tomers (Breuer et al., 2013). This gives leaders using this data an advantage over

their competitors in terms of customer loyalty and market growth. According to a

Forbes Insights and Turn report, companies investing in consumer analytics are 74

percent more likely to have a competitive advantage with customer loyalty versus 24

percent likelihood for companies that do not use consumer analytics, leading to a

three times higher likelihood of increased revenues (Alfieri, 2015).

By identifying customers with the greatest profit potential and increasing their

likelihood of purchasing the product or service o↵ering, loyalty analytics enables firms

to grow from their base (Columbus, 2014). Loyalty programs generally use discount

programs, rebate programs, or points programs to encourage customers to continue

engaging with a brand (PWC, 2013).

The consumer analytics industry is growing at a rapid rate. According to a Mar-

kets and Markets report, retail marketing is projected to grow at a CAGR rate of

39.78 percent between 2014 and 2019 from $1.8 billion in 2014 to $4.5 billion in 2019

(MarketsandMarkets, 2014). Travel, Consumer Package Goods (CPG), and retailer

are the key industries that have excelled in driving consumer analytics (Forbes In-

8

sights and Turn, 2015).

3.2 Grocery Industry

The grocery store industry in the United States is large with sales totaling $638.4

billion in 2014 (FMI, 2014). Americans spend 5.5 percent of their disposable income

on food at home with an average of 1.6 trips made to the grocery store each week

and $30.62 sales per customer transaction. Thus, the grocery store industry is a

significant portion of the economy.

The landscape of the grocery industry is competitive at the local level, yet domi-

nated by large national retailers. Grocery stores compete in localized neighborhood

markets, leading to a large number of grocery stores. Only roughly one cent for every

dollar in sales is collected as net profit for retail grocery stores. At the same time,

the top 20 grocery companies accounted for 63.8 percent of sales in 2013. The top

four accounted for 36.4 percent of sales. The top retailers are Wal-Mart, Kroger, and

Publix Supermarkets. The trend over the past twenty years is greater concentration

as the top four firms have more than doubled their share of grocery store sales during

this period (ERS, 2015).

With the growth of Amazon and other on-line grocery shopping retailers, non-

traditional grocery sales are rapidly increasing, representing 13.7 percent of the mar-

ket in 2000 and 21.5 percent of the market in 2011. Non-traditional grocers include

mail-order, home delivery, and direct sales by farmers (ERS, 2015). Online consumer

package goods sales more than doubled from 2000 to 2010, increasing from $5 billion

to $12 billion, or about 2 percent of total CPG sales (FMI 2012).

A unique characteristic of grocery stores is the multi-tiered nature of competition.

Given the vast number of options for customers within the store and the frequency

with which customers make a number of small purchase decisions, grocery stores bal-

ance a complex set of profit maximizing decisions. Stores compete between retailers

9

as in other industries. At the same time, within the store, categories compete against

each other, with di↵erent categories providing di↵erent retailer margins. Conditional

on choosing a category, brands then compete between each other. This multi-tier

competition within the store yields a complexity that is unique to the grocery indus-

try.

4 Grocery Choice

Below I summarize the multiple tiers of choices made by consumers when shopping

at the grocery store. Customers first choose which store to visit, then they choose

which aisles within the store to walk through. Finally, they decide which products to

purchase. I outline the indirect utility function that describes this process below. I

also describe how targeted coupons play a role in directing consumer store, aisle, and

product choice.

4.1 Store Choice

Each week, each household i decides whether to visit a store j and if they visit, what

groceries or households supplies to buy. Assume that in a prior period, customers de-

cide their consideration set of grocery stores. Given this consideration set, each week,

households decide which store to visit. A number of time invariant factors a↵ect the

store choice and their expenditure within the store including distance to the store,

store attractiveness, customer service in the store, availability of products relevant to

the household, and average prices of products relevant to the customer. Advertise-

ments in the form of targeted coupon campaigns can also a↵ect the likelihood that a

customer will shop at a store (Venkatesan and Farris, 2012).

Assume that household i values store j according to the indirect utility, Vijt

which

depends on time-invariant household-store match characteristics, Fij

and household

10

characteristics, wi

, receipt of targeted coupons, cijt

, and random unobserved charac-

teristics, ✏ijt

such that Vijt

= v(Fij

, w

i

, c

ijt

) + ✏

ijt

= v

ijt

+ ✏

ijt

.

Let household-store match characteristics, Fij

be a function of a number of char-

acteristics. First, the distance the household must travel to the store, distij

has been

shown to influence store choice (Hu↵, 1966; Achabal, Gorr, and Mahajan, 1982; Don-

thu and Rust, 1989; Ghosh and Craig, 1983). Second, availability of products relevant

to the customer, varietyij

, is also an important variable a↵ecting store choice (Kumar

and Leone, 1988; Messinger and Narasimhan, 1997; Kahn and Wansink, 2004; Ja-

coby and Mazursky, 1984). Thirdly, store attractiveness (including customer service,

plenty of parking, lighting, number of employees, number of checkouts), attractij

, can

induce some customers to drive to a store further away (Mehrabian and Russell, 1974;

Baker, Parasuraman, Grewal, and Voss, 2002). Finally, average prices of products

relevant to the customer, priceij

, is a factor in store choice (Bell and Lattin, 1997;

Mulhern and Leone, 1990; Ho, Tang, and Bell, 1997). The combination of these time-

invariant characteristics suggests that the household-store match characteristics can

be described by the function, Fij

= F (distij

, variety

ij

, attract

ij

, price

ij

). I control for

these match characteristics using spending and visit patterns in the control period

during which households receive no campaigns.

Next, let household characteristics, wi

, be a function of demographic characteris-

tics that might influence shopping behavior. First, household income, income

i

, can

a↵ect a household’s willingness to spend more at the store (Kalyanam and Putler,

1997; Sampson and Tigert, 1992; Hoch, et al., 1995). Second, whether the individual

is married or single, married

i

can also determine the type of food purchased (Zei-

thaml, 1985). Third, the number in household, numi

, certainly has an impact on

expected basket size (Arnold, 1997). Finally, age of the household, agei

, can also

a↵ect the number of times a household visits the store (Crask and Reynolds, 1978).

The combination of these time-invariant household characteristics can be described

11

such that wi

= w(income

i

,married

i

, num

i

, age

i

). Note that these characteristics will

likely describe the shopping patterns of overall grocery shopping. However, they may

not describe the shopping patterns in one store. Therefore, the a↵ect these charac-

teristics have on the shopping basket of households will be accounted for with control

functions.

Targeted coupons are the key variables of interest in this paper. Let targeted

coupons, cijt

be given to household i from store j during week t based on purchase

history during the control periods of the sample. Coupons are categorized as either

personalized reward, R or targeted promotions, P . Personalized rewards coupons

reward customers for their loyalty with coupons highly relevant to the customer.

Targeted promotions similarly are sent to loyal customers and are relevant to their

revealed purchase history, but they also include coupons for items less frequently

purchased in the past with the goal of acquisition for the products not purchased

before.

Loyalty rewards have been shown to increase sales for retailers (Lewis, 2004; Dreze

and Hoch, 1998). This positive response may be a result of gratitude for receiving

the coupons best suited for them (Palmateer et al., 2009). Venkatesan and Farris

(2012) give evidence that this gratitude may be shown through an exposure e↵ect

to customized coupons. Kumar and Leone (1998) also show that retailer coupons

can increase the likelihood of shoppers choosing one store over another because of

the decreased cost of shopping at that retailer. Since reward coupons are personal-

ized, this decreased cost is likely amplified since the reward coupons are matched to

customer preferences. I will test the direct and indirect of these personalized reward

coupons in this analysis. Figure 2 demonstrates how personalized reward campaigns

send coupons for frequently purchased items.

The other type of coupon of interest is the targeted promotion coupon. Promo-

tions signal information about relevant products within the store. Since retailers carry

12

Figure 2: Role of Personalized Rewards

a number of experience goods which must be consumed in order for households to de-

termine their quality, signals of product relevance within the store can increase the

indirect utility of the store to the household through the match-products-to-buyers

e↵ect (Nelson, 1974; Meurer and Stahl, 1994; Anderson and Renault, 2006). Rel-

evance can maintain the returns to advertising when retailers have an opportunity

to send targeted advertisements about products that the household is more likely to

purchase. Further, targeted promotions may be instrumental in directing the focus

of shoppers particularly when they have abstract shopping goals (Bell, Corsten, and

Knox, 2011). Targeted promotion coupons also serve to direct customers to depart-

ments highlighted on the coupon. Because coupons include products less frequently

purchased, this can encourage a customer to go to a department outside their nor-

mal shopping path, enabling them to consider a product they otherwise would not

have considered. Since personalized reward coupons only discount items frequently

purchased, they do not have the same e↵ect on directing department sales. Figure 3

demonstrates how targeted promotion campaigns send coupons for a mix of frequently

purchased items, moderately purchased items, rarely purchased items.

Since both personalized rewards and targeted promotions are matched to cus-

tomers’ previous purchase history, let targeted coupons be a function of control period

characteristics including average probability of coming to store, probcame

c

, average

13

Figure 3: Role of Targeted Promotions

spending at the store, spendc

, and frequency of purchasing products, prodfreqc

such

that cijt

= c(probcame

c

, spend

c

, prodfreq

c

). Note that the only characteristics vary-

ing across time are the targeted coupon received by household and the time since

the last trip to the store. Let ✏

it

represent unobserved disturbances to utility with

mean zero iid deviations. Utility absent the unobserved disturbances, vijt

, yields the

average utility for household i for store j in week t.

4.2 Department Choice

Once in the store, customers must decide which departments to walk through. Using

radio-frequency identification (RFID) tracking, Larson, Bradlow, and Fader (2005)

and Hui and Bradlow (2012) find that most customers stay within the perimeter of

the store and visit only a few aisles during their visit. At the same time, the longer a

customer is in the store and the more distance they travel in the store, the more likely

they are to increase their unplanned purchases (Huang, Hui, Inman, Suher, 2013).

Therefore, the more stores can attract a customer into a new department, the more

likely they are to increase spending in the store.

In store displays are an important marketing mechanism used by manufacturers to

14

direct customers’ attention (Nelson and Ellison, 2005). The goal of these displays is

to increase consideration and purchase of the advertised products and bring them into

aisles they may not have originally planned to visit. In addition to in store displays,

when a customer considers purchasing a product advertised in a targeted promotion

coupon, this may encourage the customer to walk through a department she normally

did not walk through. This may encourage the customer to go o↵ of their normal

path, enabling product consideration which would not have otherwise been possible.

I will assess the increase in sales in the departments of promoted products during

campaign weeks. Figure 4 depicts the department choice conditional on coming to

the store.

Figure 4: Conditional on their store choice, they choose which departments to walkthrough.

Conditional on coming to the store, let the expected department sales be a func-

tion of the probability of household i visiting the department, dvit

and the expected

sales conditional on visiting the department, ds

it

, such that D

it

= E(dvit

) · dsit

.

The total expected department sales are a function of targeted promotions for prod-

ucts in the department, P

itd

, redemption of targeted coupons in the department,

redeem

itd

, and other in store discounts in the department, discount

itd

such that

D

it

= D(Pitd

, redeem

itd

, discount

itd

) + ✏

it

.

15

4.3 Product Choice and Expenditure within the Store

Households vary is the degree to which they plan their purchases in the grocery

store. On average, though, a large portion of purchases are subject to in store de-

cisions. According to the Point of Purchase Advertising Institute, only 24 percent

of grocery purchases are specifically planned with the remainder a↵ected by in-store

decision making (POPAI, 2014). Inman, Winer, and Ferraro (2009) have shown that

most grocery purchases are unplanned at the category level, giving room for in store

cues. Overall expenditure within the store depends on choice of department and then

product consideration once in the department. Targeted coupons can be instrumental

in directing households to a department and prompting them to consider products

promoted in the coupons and those around them.

Most literature on unplanned purchases is limited to surveys of customers be-

fore and after they enter the store (Beatty and Ferrell, 1998; Bell, Corsten, and

Knox, 2011; Bucklin and Lattin, 1991; Inman, Winer, and Ferraro, 2009; Park, Iyer,

and Smith, 1989). Huang, Hui, Inman, Suher (2013) shed more light into point-of-

purchase drivers of unplanned consideration and purchase by tracking shoppers with

video while in the store. They observe that unplanned considerations are more likely

to turn to purchase when the shopper spends more time in consideration, engages

in more product touches, views fewer product shelf displays, stands closer to the

shelf, references external information, and interacts with store sta↵. Because people

often consider a number of options before deciding on an option, product considera-

tion is one of the more important factors which sway consumers decisions in the store

(Roberts and Lattin, 1991), accounting for up to 70 percent of the variance in a choice

(Hauser and Wernerfelt, 1989). Figure 5 demonstrates product choice conditional on

department choice.

Conditional on visiting, how much i spends at the store (i.e., the basket size) is

16

Figure 5: Conditional on a department choice, they choose to purchase certain prod-ucts.

denoted by the continuous variable s

it

. Product choice and conditional expenditure

is a function of the indirect utility, Vijt

described in the store choice above. The

mechanism through which targeted coupons a↵ect expenditure in the store, however,

is by directing households to the departments of promoted coupons and enabling more

consideration of products in that area.

5 Data Description

I have access to a unique dataset which contains the complete purchase history for

2,500 households in 369 store locations over a two-year (102 week) period from a

single unidentified retailer. Some of these households received coupons targeted to

loyal customers. The data was collected prior to 2008, so it is not a↵ected by the

overall increase in coupon usage during the recession. I observe demographic data for

801 of the households in the sample. Household demographic characteristics include

estimated age range, marital status, household income, ownership of home, and the

number of individuals in the household.

Purchase information includes the price at which customers purchased each prod-

17

Table 1: Distribution of Coupon Receipt

Number PercentNo Coupons 916 37%Only Personalized Reward 508 20%Only Targeted Promotion 71 3%Both Coupons 1,005 40%Total Households 2,500 100%

uct, the quantity purchased, and retailer, manufacturer, match, and targeted coupon

discounts. Besides personalized rewards and targeted promotions, households could

use manufacturer Product characteristics include category and brand identifiers, prod-

uct size, and whether the product was featured on a display or in a mailing that week.

The novel characteristic of the dataset is that beginning in week 37 of the first year,

the retailer began personalized reward and targeted promotion coupon campaigns to

a subset of its customers. The beginning weeks of the sample serve as a control period

during which no households receive any targeted campaigns. During the remaining

weeks of the dataset, a total of 5 personalized reward campaigns and 25 targeted

promotion campaigns were mailed to households. Table 1 outlines the distribution of

campaign recipients over the course of the test period.

There is significant variation in the number of coupon campaigns received. Thirty-

five percent of the sample received one to four total campaigns, 24 percent received

five to nine campaigns, and five percent received 10 to 17 campaigns. Although there

were up to 25 targeted promotion campaigns, the maximum number of targeted pro-

motion campaigns a household received in the sample was 12. The maximum number

of personalized reward campaigns was five. Campaigns lasted for six weeks on aver-

age, with most campaigns lasting the minimum of four weeks. Households receiving

personalized reward coupons get a simple packet of 16 coupons with a message of

18

Table 2: Coupon Discounts

Mean St. Dev. Min MaxCoupon Discount 0.97 0.54 0.08 6.00Total Weekly Discount 1.70 1.54 0.18 19.20

appreciation for their loyalty. Households receiving targeted promotion coupons get

a more colorful pamphlet with a set of coupons ranging in number between 1 (pro-

moting one specific brand) and 34; the average number of coupons sent as targeted

promotion coupons is 16.4. All campaigns highlight that this campaign is only for the

store’s best customers. Average discount for both personalized reward and targeted

promotion coupon redemptions are given in Table 2.

Redemption rates are higher for personalized reward coupons than for targeted

promotion coupons as seen in Table 3. This is appropriate since personalized rewards

are coupons for products that the customer purchases frequently. Additionally, these

coupons are on average cover a broader set of products, manufacturers, and de-

partments as Tables 4 through 6 show. While coupons in both types of campaigns

feature national manufacture brands more than private labels, more of the promotion

coupons (90.5 percent) are for national brands than personal reward coupons (86 per-

cent). Ninety-three percent of targeted promotion coupons were for one department,

with the maximum range up to 4 departments in one coupon (2 percent) while 75

percent of personalized reward coupons were for one department, with the maximum

number of departments covered in one coupon going up to 19 (5 percent).

19

Table 3: Coupon Redemptions

Coupons Total Coupon Number ofSent Redemptions Campaigns

Personalized Reward 63,664 1,791 5Targeted Promotion 53,617 527 25

Table 4: Product distribution of Coupons by Type

Reward PromotionMinimum 1 2Maximum 14,477 1,887Mean 1,200 87Number Redeemed 1,791 527%

Table 5: Manufacturer distribution of Coupons by Type

Reward PromotionMinimum 1 1Maximum 1,413 97Mean 138 3Number Redeemed 1,791 527%

Table 6: Department distribution of Coupons by Type

Department % Reward % PromotionGrocery 28.0 75.8Drug GM 16.7 15.0Meat 14.1 -Produce 12.8 0.4Meat Packaged 12.6 0.5

20

Both campaigns include a message of appreciation for the customer’s loyalty with

the coupon packet, stating that the coupons were specially chosen for the household.

The personalized reward message emphasizes that the ”exclusive o↵ers” help the cus-

tomer buy more of what they like best while the targeted promotion message suggests

that the coupons fit the shopping pattern and includes more product information and

recipes using the promoted items. Since personalized reward coupons do not convey

new information, the coupons are simple in form. Figure 6 shows an example of a

subset of coupons from a personalized reward campaign.

Since targeted promotion campaigns convey new information in line with the re-

vealed preferences of the customers, the coupons are more colorful and exhibit more

product information. Figure 7 shows an example of a targeted promotion coupon

inset with descriptions, pictures, and text about the product. Table 7 demonstrates

a representative example of the purchase history of households for coupons in a typi-

cal targeted promotion campaign. Here household 93 receives coupons for 9 product

commodities in campaign 1, and I outline the number of times the household pur-

chased these products during the control period. The pattern of frequent, moderate,

and low purchases is common across households and targeted promotion campaigns.

6 Empirical Strategy

In this section, I outline my estimation strategy for the e↵ect targeted coupons have

on sales in the store, the probability of coming to the store, and department sales. The

goal is to retrieve the e↵ect of coupons at the household level. Since targeted coupons

21

Figure 6: Personalized Reward Example

are endogenous, however, it is necessary to control for characteristics which a↵ect the

distribution of coupons. I use control functions to account for this endogeneity. In

order to determine the characteristics which best predict the receipt of coupons, I use

machine learning techniques to predict coupon distribution.

I will first outline my estimation strategy for determining the average e↵ect on

overall sales at the store. Building from this, I will assess the iterative e↵ect multiple

coupon campaigns have on each coupon type. Then, I will examine the indirect and

direct e↵ect of coupons by looking at the impact coupons have on sales conditional on

coming to the store and likelihood of coming to the store. I use the Heckit approach

developed by Heckman to control for selection of coming to the store. Finally, I will

22

Figure 7: Targeted Promotion Example

discuss the estimation of expected department sales conditional on coming to the

store controlling for the same Heckman selection.

In order to control for coupon endogeneity, I will outline the control function

approach as it applies to linear regressions and the Heckit results. Finally, I will

discuss the machine learning methods I employ, include LASSO and regression tree

analysis.

6.1 E↵ect of Coupon on Sales

Before describing the empirical approach, I will define the key variables of interest.

Let the indicator variable, vit

capture whether household i visits a grocery store in

week t. Also, conditional on visiting, how much i spends at the store (i.e., the basket

size) is denoted by the continuous variable s

it

. Finally, the expected spending at the

store in a given week is defined to be S

it

= E(sit

) = E(vit

) · sit

.

23

Table 7: Example Household Purchase History from Campaign 1

Product Commodity Description Number Times Purchased in ControlFrozen Meat & Meat Dinners 61Canned Juices 20Cheese 20Yogurt 6Frozen Novelties 5Condiments/Sauces 2Milk by-Products 2Refrigerated Juices 1Vegetables-Stable Shelf 1

There are two types of targeted coupons, personalized rewards and targeted pro-

motions. Assume there are C

t

coupons o↵ered at time t where each coupon can be

categorized as either personalized reward, Rt

, or targeted promotion, Pt

such that

C 2 {Rt

, P

t

}. Let x

iRt

be an indicator for whether a personalized reward coupon

was o↵ered to household i at time t and x

iP t

similarly indicate if a targeted pro-

motion coupon was o↵ered. Collect the coupon o↵ers valid for week t in a vector

�!x

it

= (xiRt

, x

iP t

). The vector�!R

it

= (RiRt

, R

iP t

) captures redeemed reward or promo-

tion coupons in week t. Let other discounts be captured in the vector�!d

it

where other

discounts can be in the form of retail discounts, other coupon discounts, or match

discounts.

6.1.1 Regression of Expected Sales

The first step in assessing the e↵ect of coupon on sales is to get an overall view

of how the coupons a↵ect expected sales at the grocery retailer. Targeted coupons

sent to households serve as a form of advertisement to the household for the store

a↵ecting the probability a customer visits the store and the basket size conditional

on coming to the store. As outlined above, the combination of these two e↵ects can

be summarized in the expected sales at the store in a given week.

24

As described in the store choice model above, the key factors in determining ex-

pected sales include the time-invariant household-match characteristics, Fij

, house-

hold characteristics, wi

, time since the last trip, sit

, targeted coupons, cijt

, and random

unobserved characteristics. Since seasonality a↵ects grocery store purchase patterns,

I account for the seasons of the year with for indicator variables, s1

, s

2

, s

3

, s

4

. I also

account for the number of weeks since the last visit to the store, weeksi

.

In a given week, if a household receives a personalized reward coupon in the mail,

this is reflected by the indicator, xiRt

. If a household receives a targeted promotion,

this is reflected by x

iP t

. I model the expected sales in a given week t unconditional

on coming to the store using fixed e↵ects regression where

S

it

= x

iRt

�

iR

+ x

iP t

�

iP

+ s

1

+ s

2

+ s

3

+ u

it

. (1)

Accounting for the endogeneity of personalized reward and targeted promotion

coupons, I use control functions to predict the likelihood that household i has re-

ceived at least one coupon during the sample using prediction characteristics outlined

above such that x

iRt

= Pr(P

102

t=1

x

iRt

� 1) = c(probcame

c

, spend

c

, prodfreq

c

). The

household characteristics control for the time-invariant household-store match and

household characteristics.

The above equation provides an average e↵ect of each type of coupon on household

purchase behavior during the weeks of the campaign. However, it is possible that

households react di↵erently to successive campaigns. This is especially important

when trying to assess the di↵erential impact of each type of targeted coupon. To

get a better sense of how iterative personalized reward campaigns impact spending

behavior, I model expected sales in a given week t unconditional on coming to the

store where

S

it

= x

iP t

�

iP

+x

iR1t

�

iR1

+x

iR2t

�

iR2

+ ...+x

iR5t

�

iR5

+w

i

+days

i

+s

1

+s

2

+s

3

+u

it

, (2)

25

where x

iRkt

represents the k

th personalized reward coupon a household receives

during the test period and x

iP t

controls for receipt of targeted promotion campaigns.

Similarly, I model the iterative e↵ect of targeted promotion coupons with

S

it

= x

iRt

�

iP

+x

iP1t

�

iP1

+x

iP2t

�

iP2

+...+x

iP12t

�

iP12

+w

i

+days

i

+s

1

+s

2

+s

3

+u

it

. (3)

There are five personalized reward campaigns during the sample, and households

receive between zero and five personalized reward campaigns. While there are twenty-

five targeted promotion campaigns, the maximum number of targeted promotion

campaigns one household receives in the sample is twelve.

6.1.2 Heckit Regression of Expected Sales

In order to di↵erentiate between change in likelihood of coming to the store and

change in spending conditional on coming to the store, I account for selection using

an approach developed by Heckman (1976). This model is particularly useful since

the targeted coupons sent to households’ homes in the mail a↵ect both the likelihood

of coming to the store and household spending conditional on coming to the store.

However, once in the store, other factors also a↵ect spending, including redemption

of coupons and other store discounts.

Assume that sales in week t, Sit

conditional on coming to the store, linearly de-

pends on the targeted coupons received in a given week, �!xit

, redemptions of each

type of coupon, redeemiRt

and redeem

iP t

, other in store discounts including retail

discount, retail

it

, other coupon discounts, coupon

it

, and coupon match discounts,

match

it

, (summarized as discountit

) time invariant household characteristics are cap-

tured through the control functions, controli

, and seasonal controls for s1

, s2

, and s

3

such that

S

it

= x

iRt

�

iR1

+ x

iP t

�

iP1

+ redeemit↵i + discountit�i + controli✓i + s�i + u

it

. (4)

26

Also, let the indicator variable, vit

capture whether household i visits a grocery

store in week t. Assume that store visit is a function of the targeted coupons received

in a given week, �!xit

, time invariant household characteristics are captured through

the control functions, controli

, and seasonal controls for s1

, s2

, and s

3

such that

v

it

= x

iRt

�

iR2

+ x

iP t

�

iP2

+ controli✓i + s�i + v

it

. (5)

With the following set of assumptions, we are able to control for selection using

the Heckit approach. Assuming, �!xit

and v

it

are always observed, sales, Sit

, are only

observed when a household visits a store (or vit

= 1), (uit

, v

it

) is independent of �!xit

with zero mean, vit

⇠ Normal(0, 1), and E(uit

|vit

) = �

1

v

it

.

Since the conditional expectation of sales given a household visits the store in a

given week can be summarized as

E(Sit

|�!xit

, v

it

= 1) = �!x

it

�

1

+ ⇢�(�!xit

�

2

) (6)

where �(·) = �(·)�(·) is the Inverse Mills ratio, we can proceed with the Heckit two

step approach in order to first estimate �2

and then obtain the estimated inverse Mills

ratio and finally estimate �

1

and ⇢. Formally, I first obtain the probit estimate �

2

from the model

Pr(vit

= 1|�!xit

) = �(�!xit

�

2

). (7)

This allows me to compute the estimated inverse Mills ratio, � = ˆ�(�!x

it

ˆ )�2

. Finally,

I estimate �1

and ⇢ from the OLS regression on the selected sample including the orig-

inal variables listed in 4 and �. The estimators are consistent andpN -asymptotically

normal.

27

6.1.3 Department Sales

I employ the same Heckit approach when estimating the department sales conditional

on coming to the store. Here, the total expected department sales are a function of

targeted promotions for products in the department, xiPdt

, redemptions either type

of targeted coupon, redeemidt

, other department discounts including retail discount,

retail

idt

, other coupon discounts, couponidt

, and coupon match discounts, match

idt

,

(summarized as discount

idt

) time invariant household characteristics are captured

through the control functions, controli

, department fixed e↵ects, di

, where i 2 [1, 43],

and seasonal controls for s1

, s2

, and s

3

such that

D

it

|vit

= 1 = x

iPdt

�

iPd

+ redeem

idt

↵

id

+discountidt�id + controli✓i +dµ+ s�i + u

it

.

(8)

6.2 Control function for Coupon Selection

6.2.1 Endogenous Targeted Coupons

Since the retailer’s targeted coupon strategy relies on building and retaining loyalty,

both personalized rewards and targeted promotions are primarily sent to their most

loyal customers. Once the set of customers is established, the retailer then sends

coupons relevant to these customers. Therefore, the number of targeted coupons sent

to customers is endogenous since higher levels of loyalty are correlated with higher

coupon receipt and higher spending once the household receives the coupon. In order

to assess the impact of coupons on spending behavior, it is imperative to control for

the factors that a↵ect coupon receipt.

It is typically di�cult to disentangle the e↵ects of targeted coupons from the

targeting endogeneity (Nair et al., 2014) because the likelihood of a customer being

targeted is simultaneously assessed with the impact of the targeted campaign. On

28

Table 8: Control Period

Prob Visit Store St. Dev. Mean Weekly Sales St. Dev.No Campaigns 21% 41% $8.23 $27.201-4 Campaigns 43% 50% $22.87 $44.035-9 Campaigns 59% 49% $47.65 $67.1210-17 Campaigns 68% 46% $81.42 $93.47

Table 9: Test Period

Prob Visit Store St. Dev. Mean Weekly Sales St. Dev.No Campaigns 26% 44% $10.93 $33.281-4 Campaigns 61% 49% $33.49 $50.485-9 Campaigns 83% 37% $74.50 $74.7610-17 Campaigns 90% 31% $124.26 $96.90

average, customers that receive more targeted coupons, spend more at the store and

come more regularly. This pattern is reflected in the control period spending and

visit patterns of households in Table 8. Without controlling for the endogeneity of

coupon receipt, it is possible to inflate the e↵ectiveness of targeted coupons relative

to no coupons by comparing the sales of the two groups. Average sales during the

test period are shown in Table 9.

The unique feature of this dataset enables me to disentangle these two e↵ects

by controlling for the targeting likelihood of a customer. In particular, I use the

purchase and visit patterns in the control period, during which no coupon campaigns

are sent, to predict the likelihood of coupon receipt during the test period. I then use

this prediction to control for the endogenous characteristics of coupon receipt when

assessing the impact on the sales during the campaigns.

29

In the following section, I will outline the control function approach. Next I

will discuss my use of machine learning methods to identify the loyalty tiers used

to determine targeted coupon allocation. Finally, I will present the results of the

targeted coupon prediction.

6.2.2 Control Function Strategy

In order to address the endogeneity of targeted coupons when assessing the impact

of the coupon on sales, I will use control functions. Following Wooldridge (2007), for

S

it

weekly sales, endogenous �!x

it

, and �!z

it

vector of exogenous variables, we have the

model

S

it

= �!z

it

� +�!x

it

↵ + u

1

. (9)

where the expected spending at the store in a given week is defined to be S

it

=

E(sit

) = E(vit

) · sit

. Let �!zit

be exogenous in the sense that it satisfies orthogonality

conditions such that E(z0u1

) = 0. As is the case in 2 stage least squares, the linear

projection of xit

onto exogenous variables plays a critical role The reduced form of

x

it

can be written as

x

it

= z

it

⇡

2

+ v

2

(10)

where E(zit

0v

2

) = 0. The endogeneity of �!xit

implies that it does not satisfy the zero

covariance condition, E(x0u

1

) 6= 0. This means that u1

is correlated with v

2

. We can

write the linear projection of u1

on v

2

in error form as

u

1

= ⇢

1

v

2

+ e

1

, (11)

where ⇢

1

= E(v2u1)

E(v

22)

. Since �!z

it

is uncorrelated with u

1

and v

2

, we have E(v2

e

1

)) = 0

and E(z0e1

) = 0. Plugging in equation 11 into equation 9 gives us

S

it

= �!z

it

� +�!x

it

↵ + ⇢

1

v

2

+ e

1

(12)

30

where v

2

can be seen as an explanatory variable in the equation. As noted above, e1

is uncorrelated with both v

2

and z. Also, since �!xit

is a linear function of both �!z

it

and

v

2

, so e

1

is also uncorrelated with �!x

it

. Since we do not observe v

2

we must estimate

it using the first stage regression of xit

on z

it

in equation 10. We then recover the

residual, v2

= x

it

� z

it

⇡

2

. Substituting the residual into 12 gives us

S

it

= �!z

it

� +�!x

it

↵ + ⇢

1

v

2

+ error, (13)

where for each i, errori

= e

i1

+ ⇢

1

z

it

(⇡2

� ⇡

2

), which depends on the sampling error

in ⇡

2

unless ⇢1

= 0. This approach gives consistent control estimates for �, ↵, and ⇢

1

.

Here the inclusion of v2

controls for the endogeneity of �!xit

in 9, although with some

sampling error since ⇡

2

6= ⇡

2

.

In the case of non-linear functions such as profit, Blundell-Smith (1986) and

Rivers-Vuong (1988) have developed a two step approach of first applying OLS of

�!x

it

on �!z

it

to obtain the residual, v2

and second running probit of Sit

on �!z

it

, �!xit

, and

v

2

to estimate the scaled coe�cients.

This approach makes a homoskedastic-normal assumption on the reduced form of

�!x

it

,

x

it

= z

it

⇡

2

+ v

2

, v

2

|z ⇠ Normal(0, ⌧ 2),

requires (u1

, v

2

) independent of z, and assumes joint normality, (u1

, v

2

) ⇠ BivariateNormal

with ⇢

1

= Corr(u1

, v

2

). This is equivalent to assuming

D(u1

|v2

, z

it

) = Normal(✓1

v

2

, 1� ⇢

2

1

)

where ✓

1

= ⇢1

⌧2is the regression coe�cient. Thus, the original coe�cients can be

retrieved from the 2-step procedure such that

�1 =�

⇢1

(1 + ✓

2

⇢1

⌧

2

2

)12

31

where ✓

2

⇢1

is the coe�cient on v

2

, ⌧ 22

is the error variance estimator from the first

stage OLS, and �

⇢1 contains �

⇢1

and ↵

⇢1

. I use linear probability in the first stage

because it is impossible to get consistent estimates in a 2-stage estimation including

non-linear estimation in both stages (Wooldridge 2007).

6.2.3 Machine Learning Methods

In order to determine the variables that have the highest predictive power for the

number of coupons received, I use machine learning methods to identify the variables

most highly correlated with coupon receipt. The retailer cites loyalty as the most

important driver for determining targeted coupon distribution. Since I do not observe

loyalty status, I use LASSO to identify the household variables most correlated with

the number of campaigns received. LASSO enables me to narrow my focus on the

most relevant variables that predict coupon receipt, providing more predictive power

for the control function than if I just throw all of the variables in together. By

narrowing down my focus on the variables that actually matter (and coincide with

the variables that the company states they use), I get better estimates. I outline the

method below.

After selecting average sales and average days between visits as the most impor-

tant variables, I then map the distribution of coupon receipt across households using

a regression tree. This enables me to subset tiers of sales and visits into buckets

and create loyalty measures for customers. Regression trees helps refine the focus

even more so that instead of using a continuous variable of for example, the average

spending in a week during the control period, I can see the specific weight put on

people who spend over $90, between $50 and $90, and so on.

Least Absolute Shrinkage and Selection Operator (LASSO) is a method estimating

regression function via penalization and selection. This model selection method is

useful in identifying the relevant variables in a regression by minimizing squared

32

errors, while penalizing the size of the model through by the sum of absolute values

of coe�cients. This approach zeros out a number of irrelevant regressors. I employ a

two step approach to avoid a bias in estimates toward zero. First, I select the model

using LASSO. Second, I apply ordinary least squares to the selected model.

Following Frank and Friedman (1993) and Tibshirani (1996) I use the `� 1 norm

for the penalization. The LASSO estimator, �, minimizes

1

n

Pn

i=1

(yi

� x

i

�)2 + �k�k1

, wherek�k1

=P

p

j=1

���

j

��.

The ` � 1 norm ensure that the minimization problem is globally convex and

the kink in the penalty induces the solution � to have many zeros. Bickel, Ritov,

Tsybakov (Annals of Statistics, 2009) show that the rate-optimal penalty choice is

� = �2q

2log(pn)

n

, where p is the number of possible regressors and n is the number

of observations. However, this requires the knowing � which is di�cult when p ⌧ n.

I use cross-validation, which has been shown to perform well in Monte-Carlo, to

estimate �.

The rate of convergence for LASSO estimates are���� � �

0

��� .q

1

n

Pn

i=1

[x0i

� � x

0i

�

0

]2 .

�

qs log(max{n,p})

n

, which is close to the ratep

s

n

when the true model is known.

Here, s is the number of non-zero regression coe�cients satisfying the condition,

s

:=P

p

j=1

�

1j

6= 0 ⌧ n (Bickel, Ritov, Tsybakov, 2009). Post-LASSO performs at

least as well, with convergence up to �

ps

n

.

Regression trees are another powerful tool identify patterns in data. This method

is particularly useful when data has a number of features that interact in complicated,

non-linear ways, making the assembly of one global model di�cult. The regression

tree approach partitions the space into smaller regions where interactions are more

manageable. Recursive partitioning continues to partition sub-divisions until it is

possible to fit simple models to the remaining space. The global model has two parts:

the recursive partition and the simple model for each cell of the partition.

33

The regression tree represents the recursive partition results. Each of the terminal

nodes, or leaves of the tree, represents a cell of the partition. Starting at the root

node of the tree, one can answer a series of questions about attributes. Interior nodes

represent the questions and the branches represent the answers. The simple model for

each cell of the partition is a constant estimate of the dependent variable. The simple

estimate is generally the sample mean of the dependent variable for the partitioned

values in that leaf, yl = 1

c

Pc

i=1

y

i

, where c is the number of observations in leaf-node

l. This is a piecewise-constant model that can provide a jagged-response when the

true regression surface is not smooth. Construction of the tree starts with a question

that maximizes the information about the dependent variable. This gives the root

node and two daughter nodes. This procedure is repeated at each daughter node.

A typical stopping criterion stops the growth of the tree when new information is

minimal.

7 Results

7.1 Predicting Coupon Receipt

I employ LASSO to identify the variables that are most highly correlated with the out-

come variable of the number of campaigns received. Because the two targeted coupon

campaign types are distinct in focus, I divide analysis between the personalized re-

ward campaigns and the targeted promotion campaigns. Since personalized reward

coupons are intended to surprise and delight the grocery retailer’s most loyal cus-

tomers, these campaigns are sent primarily to customers exhibiting the most loyalty

to the store. While targeted promotion coupons have an acquisition component, they

are sent to customers with a wider level of loyalty to the firm. Here, demographic

characteristics and purchase patterns of particular products are more predictive of

coupon receipt.

34

Input variables are all calculated during the control period of the dataset and

include average weekly sales and average number of days between visits. I also include

two variables to capture the likelihood of being in a campaign by the match value of

top campaigned categories (in at least 10 campaigns) and the regularity of purchasing

these products. For the top categories, I identify if a household has purchased this

item regularly (at least 1 percent of all products purchased, accounting for the top

half of products purchased) or frequently (at least 3 percent of all products purchased,

accounting for the top 15 percent of products purchased). Finally, I include a list of

demographic characteristics including household size, whether someone is married, a

homeowner, age group, and income group.

In the first stage of identifying the model that best predicts coupon receipt, I run

LASSO of the number of personalized reward or targeted promotion campaigns the

household receives during the course of the sample on the input variables listed above.

Then I use the Regression Tree to identify patterns among the remaining regressors.

Finally, I run a regression of final set of variables on the number of personalized

reward or targeted promotion campaigns, dummies of whether the household received

at least one campaign on each type, and dummies of whether the household received

k campaigns of each type.

7.1.1 Personalized Reward Campaigns

The only variables that receive weight in the LASSO regression of the number of

personalized reward campaigns on the list of input variables above are the average

sales and the average number of days between visits in the control function. Cross

validation yielded � = 0.382. The first stage LASSO estimates are biased toward

zero, so the coe�cient values in Table 10 should not be used to interpret marginal

e↵ects. Rather, the purpose of this first stage is to identify the variables that are

most predictive.

35

Table 10: LASSO Regression: Number of Personalized Reward Campaigns

Input Variable Coe�cientAve Weekly Sales 0.01Ave Days Between Visits -0.04

This prediction coincides with the retailer’s goal of rewarding loyalty in order to

build loyalty. Since competition amongst grocery retailers is strong, grocery retailers

try to di↵erentiate themselves from others by increasing the share of wallet they are

able capture from customers. Share of wallet is the percentage of sales made in one

retailer relative to all of the grocery purchases a customer makes at all grocery stores

in a given week. Higher wallet share reflects higher customer loyalty.

There are two key measurements of loyalty in the grocery industry. First, weekly

sales capture the overall value that a customer has for a store. Customers that spend

more on average are worth more than customers that spend less. They are also more

loyal because there is a higher likelihood that the customer is purchasing most of her

groceries at that retailer. Second, visit patterns also capture the level of loyalty a

customer has for a store. For example, even if a customer does not spend that much

at a store, but the customer comes almost every day, that customer is likely spending

most of its grocery spending at one retailer.

Since loyalty is the relevant variable of interest, it is important to organize average

sales and average days between visits into tiers. I use the regression tree to identify

significant cuto↵ points in the data. For average weekly sales, significant cut o↵

points are low sales at $13, which represents the top 45% of the sample, low-medium

sales at $25, which represents the top 68% of the sample, medium sales at $50,

which represents the top 87%, and high sales at $90, which represents the top 96%

of the sample. Results are summarized in Table 11 For average days between visits,

36

Table 11: Regression Tree Cut O↵s: Ave Weekly Sales

Ave Weekly Sales Cut O↵ Label $13 Low Sales$13–$25 Low Medium Sales$25–$50 Medium Sales$50–$90 Medium High Sales� $90 High Sales

Table 12: Regression Tree Cut O↵s: Ave Days Between Visits

Ave Weekly Sales Cut O↵ Label 3 Days Daily3–7 Days Twice Weekly7–10 Days Weekly10–16 Days One & Half Weekly16–35 Days Bi-Weekly� 35 Days Infrequent

significant cut o↵ points are 3 days, which represents the top 92% of customers in

the sample, 7 days, which represents the top 70% of the customers, 10 days, which

represents the top 57% of the customers, 16 days, which represents the top 40% of the

customers, and 35, which represents the top 15% of the sample. Table 12 summarizes

results.

Using the constructed cuto↵ points, I first regress the number of personalized re-

ward campaigns on the set of average weekly sale and frequency of visit dummies,

and then I regress the dummy for receiving at least k campaigns, where k 2 [0, 5].

While these variables predict the number of campaigns well, it is still not a perfect

fit. This is because there remains some randomness in the campaign distribution

process. The retailer intentionally establishes a number of control and test groups

37

even within groups with similar loyalty levels in order to e↵ectively compare house-

holds of comparable status with varying campaigns. This allows the retailer to more

accurately assess the e↵ectiveness of the campaigns. Therefore, the prediction model

accounts for the endogenous components of the campaign prediction model. Regres-

sion estimates are shown in Table 13. The R

2 for the number of personalized reward

campaigns is 0.54, for � 1 Camp is 0.43, for � 2 Camp is 0.48, for � 3 Camp is

0.41, for � 4 Camp is 0.22, and for � 5 Camp is 0.15. Residuals in this first stage

regression for control functions will be used in the estimation of coupon e↵ect on

sales. The main concern with using linear probability is that it may be possible to

predict results outside the bounds of 0 and 1. In this case, since a household must be

in one frequency bucket and one average sales bucket, the likelihood is the sum of the

marginal e↵ects of these two categories. Since there are no combinations which sum

to greater than or less than one, we avoid the problem of out of bounds predictions.

7.1.2 Targeted Promotion Campaigns

Since targeted promotion campaigns are a combination of acquisition and reward

coupons, a slightly broader set of variables are selected by the LASSO regression.

In addition to control period characteristics of average weekly sales and the average

days between visits, the number of top couponed products that households regularly

purchase was also selected. Because campaigns match the purchase pattern of house-

holds to a set of coupons, the higher the match value, the higher likelihood of receiving

coupons from the campaign. Here, the cross validation � = 0.069. Predicted variables

are shown in table 14.

38

Table 13: Personal Reward Prediction

Num Camp � 1 Camp � 2 Camp � 3 Camp � 4 Camp � 5 CampDaily 1.54⇤⇤⇤ 0.426⇤⇤⇤ 0.418⇤⇤⇤ 0.311⇤⇤⇤ 0.228⇤⇤⇤ 0.156⇤⇤⇤

(0.12) (0.041) (0.041) (0.041) (0.031) (0.024)Twice Weekly 1.481⇤⇤⇤ 0.424⇤⇤⇤ 0.425⇤⇤⇤ 0.363⇤⇤⇤ 0.19⇤⇤⇤ 0.078⇤⇤⇤

(0.094) (0.032) (0.032) (0.032) (0.025) (0.018)Weekly 1.079⇤⇤⇤ 0.422⇤⇤⇤ 0.352⇤⇤⇤ 0.239⇤⇤⇤ 0.056⇤ 0.01

(0.095) (0.033) (0.032) (0.032) (0.025) (0.019)One & Half Weekly 0.62⇤⇤⇤ 0.31⇤⇤⇤ 0.208⇤⇤⇤ 0.115⇤⇤⇤ �0.003 �0.011

(0.082) (0.028) (0.028) (0.028) (0.021) (0.016)Bi-Weekly 0.146⇤ 0.11⇤⇤⇤ 0.042 0 �0.004 �0.002

(0.073) (0.025) (0.025) (0.025) (0.019) (0.014)Low Medium Sales 0.781⇤⇤⇤ 0.327⇤⇤⇤ 0.273⇤⇤⇤ 0.162⇤⇤⇤ 0.014 0.004

(0.064) (0.022) (0.021) (0.022) (0.017) (0.013)Medium Sales 1.314⇤⇤⇤ 0.386⇤⇤⇤ 0.432⇤⇤⇤ 0.369⇤⇤⇤ 0.091⇤⇤⇤ 0.035⇤

(0.076) (0.026) (0.025) (0.026) (0.02) (0.015)Medium High Sales 1.731⇤⇤⇤ 0.415⇤⇤⇤ 0.489⇤⇤⇤ 0.465⇤⇤⇤ 0.214⇤⇤⇤ 0.148⇤⇤⇤

(0.10) (0.034) (0.034) (0.034) (0.026) (0.02)High Sales 1.673⇤⇤⇤ 0.378⇤⇤⇤ 0.466⇤⇤⇤ 0.481⇤⇤⇤ 0.195⇤⇤⇤ 0.154⇤⇤⇤

(0.142) (0.049) (0.048) (0.048) (0.037) (0.028)Constant 0.209⇤⇤⇤ 0.141⇤⇤⇤ 0.048⇤ 0.021 0 0

(0.057) (0.02) (0.019) (0.02) (0.015) (0.011)Standard errors in parentheses⇤ p < 0.05, ⇤⇤ p < 0.01, ⇤⇤⇤ p < 0.001

Table 14: LASSO Regression: Number of Targeted Promotion Campaigns

Input Variable Coe�cientAve Weekly Sales 0.0440Ave Days Between Visits -0.0029Regularly Bought Top Couponed Products 0.0473

39

I regress the number of number of targeted promotion campaigns on the set of

average weekly sale and frequency of visit dummies along with the number of top

couponed products regularly purchased by the household. Then I regress the dum-

mies for receiving at least k campaigns, where k 2 [0, 12]. Again, while these variables

predict well, they do not provide a perfect fit particularly because of the acquisition

aspect of the campaigns. There is more use of control and testing in order to iden-

tify which campaigns produce the best results. This adds more randomness to the

distribution of targeted promotion coupons. Furthermore, since the number of house-

holds receiving higher number of campaigns diminishes significantly (for example, 11

households received 9 campaigns, 8 households received 10 campaigns, 3 households

receive 11, and 2 households receive 12), precision is lost for estimation of receiving

at least k campaigns when k is larger.

Regression estimates are shown in Table 15. The R2 for the number of personalized

reward campaigns is 0.39, for � 1 Camp is 0.30, for � 2 Camp is 0.33, for � 3 Camp

is 0.30, for � 4 Camp is 0.27, for � 5 Camp is 0.24, for � 6 Camp is 0.20, for � 7

Camp is 0.16, for � 8 Camp is 0.10, for � 9 Camp is 0.07, for � 10 Camp is 0.05,

for � 11 Camp is 0.02, and for � 12 Camp is 0.01. Residuals in this first stage

regression for control functions will be used in the estimation of coupon e↵ect on

sales. The main concern with using linear probability is that it may be possible to

predict results outside the bounds of 0 and 1. In this case, since a household must be

in one frequency bucket and one average sales bucket, the likelihood is the sum of the

marginal e↵ects of these two categories. Since there are no combinations which sum

to greater than or less than one, we avoid the problem of out of bounds predictions.

40

Tab

le15:TargetedPromotionPrediction

Num

Cam

p�

1Cam

p�

2Cam

p�

3Cam

p�

4Cam

p�

5Cam

p�

6Cam

p�

7Cam

p�

8Cam

p�

9Cam

p�

10Cam

p�

11Cam

p�

12Cam

pDaily

0.365⇤

0.116⇤

0.085⇤

0.045

0.028

0.005

0.018

0.031

0.027

0.004

0.010

0.001

�0.005

(0.182)

(0.047)

(0.042)

(0.038)

(0.033)

(0.029)

(0.024)

(0.020)

(0.015)

(0.011)

(0.008)

(0.005)

(0.003)

TwiceWeekly

0.260

0.141⇤

⇤⇤0.060

0.030

0.017

0.011

0.010

0�0.009

0.001

0�0.001

0(0.143)

(0.037)

(0.033

(0.030)

(0.026)

(0.023)

(0.019)

(0.016)

(0.012)

(0.008)

(0.006)

(0.004)

(0.004)

Weekly

0.229

0.163⇤

⇤⇤0.055

0.014

0.005

0.002

0.002

�0.011

�0.008

00.001

0.002

0.002

(0.145)

(0.037)

(0.033)

(0.030)

(0.027)

(0.023)

(0.019)

(0.016)

(0.012)

(0.008)

(0.006)

(0.004)

(0.003)

One&

HalfWeekly

0.024

0.073⇤

�0.010

�0.026

�0.001

�0.006

�0.007

00.003

�0.001

�0.001

00

(0.126)

(0.032)

(0.029)

(0.026)

(0.023)

(0.020)

(0.016)

(0.014)

(0.011)

(0.007)

(0.005)

(0.003)

(0.002)

Bi-Weekly

0.004

0.042

0.010

�0.009

�0.0130

�0.017

�0.004

�0.005

�0.002

00.001

0.002

0(0.111)

(0.029)

(0.026)

(0.023)

(0.021)

(0.018)

(0.015)

(0.012)

(0.009)

(0.007)

(0.005)

(0.003)

(0.002)

Low

Medium

Sales

0.547⇤

⇤⇤0.235⇤

⇤⇤0.136⇤

⇤⇤0.082⇤

⇤⇤0.062⇤

⇤⇤0.023

0.007

0.003

0.001

�0.001

�0.001

�0.001

0(0.097)

(0.025)

(0.022)

(0.020)

(0.018)

(0.015)

(0.013)

(0.011)

(0.008)

(0.006)

(0.004)

(0.003)

(0.002)

Medium

Sales

1.509⇤

⇤⇤0.430⇤

⇤⇤0.415⇤

⇤⇤0.287⇤

⇤⇤0.171⇤

⇤⇤0.093⇤

⇤⇤0.055⇤

⇤⇤0.030⇤

0.018

0.003

0.002

0.001

0.002

(0.115)

(0.029)

(0.026)

(0.024)

(0.021)

(0.018)

(0.015)

(0.013)

(0.010)

(0.007)

(0.005)

(0.003)

(0.002)

Medium

HighSales

3.046⇤

⇤⇤0.605⇤

⇤⇤0.623⇤

⇤⇤0.567⇤

⇤⇤0.469⇤

⇤⇤0.336⇤

⇤⇤0.192⇤

⇤⇤0.141⇤

⇤⇤0.081⇤

⇤⇤0.027⇤

⇤0.005

00.001

(0.151)

(0.039)

(0.035)

(0.032)

(0.028)

(0.024)

(0.020)

(0.017)

(0.013)

(0.009)

(0.007)

(0.004)

(0.003)

HighSales

4.460⇤

⇤⇤0.646⇤

⇤⇤0.702⇤

⇤⇤0.655⇤

⇤⇤0.627⇤

⇤⇤0.565⇤

⇤⇤0.467⇤

⇤⇤0.329⇤

⇤⇤0.202⇤

⇤⇤0.133⇤

⇤⇤0.085⇤

⇤⇤0.034⇤

⇤⇤0.014⇤

⇤⇤

(0.214)

(0.055)

(0.049)

(0.045)

(0.039)

(0.034)

(0.028)

(0.023)

(0.018)

(0.012)

(0.009)

(0.006)

(0.004)

Reg

Buy

0.075⇤

⇤⇤0.014⇤

⇤0.015⇤

⇤⇤0.013⇤

⇤⇤0.009⇤

⇤0.009⇤

⇤⇤0.006⇤

⇤0.005⇤

⇤0.002

0.001

00

0(0.016)

(0.004)

(0.004)

(0.003)

(0.003)

(0.003)

(0.002)

(0.002)

(0.001)

(0.001)

(0.001)

(0)

(0)

Con

stan

t0.003

0.074⇤

⇤0.004

�0.005

�0.015

�0.016

�0.017

�0.012

�0.005

�0.003

�0.001

00

(0.096)

(0.025)

(0.022)

(0.020)

(0.018)

(0.015)

(0.012)

(0.010)

(0.008)

(0.006)

(0.004)

(0.003)

(0.002)

Standarderrors

inparentheses

⇤p<

0.05

,⇤⇤

p<

0.01,

⇤⇤⇤p<

0.00

1

41

7.2 E↵ect of Coupon on Sales

The purpose of this paper is to assess the di↵erential impact of personalized reward

and targeted promotion campaigns on store sales. I begin this analysis by looking at

the average e↵ect each coupon has on expected sales in a week. Looking deeper into

the patterns each coupon has with successive campaigns, I assess the iterative impact

each targeted coupon has on expected sales. I find that targeted promotion cam-

paigns outperform personalized rewards in each scenario, with personalized reward

coupons exhibiting decreasing returns and targeted promotion coupons demonstrat-

ing increasing returns during the weeks of the campaigns.

Since coupons a↵ect both the likelihood of coming to the store and the sales con-

ditional on coming to store, I next decompose the impact on each, including other

in-store factors such as redemption of the coupons and other discounts. Control-

ling for selection enables me to estimate the unbiased e↵ect of the coupons in each

grocery choice stage. I find that while both coupons increase the likelihood of cus-

tomers visiting the store during campaign weeks, targeted promotion coupons attract

customers better than personalized reward coupons. Additionally, while personal-

ized reward coupons increase sales more when customers redeem these coupons, the

spillover e↵ect of targeted promotion coupons is higher. Finally, I test whether the

spillover e↵ect is concentrated in the department of the promoted coupons, and I find

evidence which supports this hypothesis.

7.2.1 Average E↵ect on Expected Sales

In order to get an overall view of how targeted coupons sent to households a↵ect

expected sales at the grocery retailer, I regress weekly sales on the receipt of person-

alized reward and targeted promotion campaigns during the weeks of the campaign.

Since targeted coupons serve as a form of advertisement to the household, they a↵ect

42

the probability of visiting the store in a given week and the basket size conditional

on coming to the store. The combination of these e↵ects can be summarized in the

expected sales at the store in a given week. Using the coupon prediction model, I

include predictors for personalized reward and targeted promotion campaigns. I also

include the residuals for the k = 1 regressions for each campaign type, representing

receipt of at least one personalized reward or targeted promotion campaign.

Results are outlined in Table 16. Targeted promotion campaigns increase expected

weekly sales on average by $14.08, which is three times as high as the marginal e↵ect

of personalized reward campaigns at $3.65. Understanding the overall marginal e↵ect

of the coupons on expected sales provides a baseline from which to analyze further

analysis on successive campaigns, di↵erentiating the e↵ect on probability of coming

to store and expected sales conditional on coming to the store, and department sales.

This result is surprising since redemption rates of personalized reward coupons far

surpass those of targeted promotion coupons. Examination of the redemption rates

alone may lead one to presume that personalized reward coupons perform better than

targeted promotion coupons. Additionally, the underlying presumption in targeting

is that often that the closer the targeted advertisement is to the preferences of the

individual, the more likely they are to respond positively. This result suggests that

the positive response via higher redemption may not be the most important factor

to examine. While this result cannot identify the driver of the di↵erential impact, it

suggests that there is more to examine.

7.2.2 Average Iterative E↵ect on Expected Sales

One of the key reasons for rewarding customers is to build and maintain loyalty.

Reward coupons are meant to generate gratitude among customers that may lead

43

Table 16: Average E↵ect on Expected Sales

Weekly SalesPersonalized Reward 3.65⇤⇤⇤

(0.32)Targeted Promotion 14.08⇤⇤⇤

(0.41)Daily �0.04

(0.54)Twice Weekly �1.57⇤⇤⇤

(0.43)Weekly �0.74

(0.43)One & Half Weekly �1.23⇤⇤

(0.38)Bi-Weekly �0.34

(0.33)Low Medium Sales 10.87⇤⇤⇤

(0.29)Medium Sales 28.85⇤⇤⇤

(0.35)Medium High Sales 56.79⇤⇤⇤

(0.45)High Sales 109.77⇤⇤⇤

(0.64)Regularly Bought �2.40⇤⇤⇤

(0.05)v

R1

3.94⇤⇤⇤

(0.29)v

P1

11.95⇤⇤⇤

(0.26)S2 6.51⇤⇤⇤

(0.27)S3 6.93⇤⇤⇤

(0.27)S4 8.49⇤⇤⇤

(0.27)Constant 13.82⇤⇤⇤

(0.34)Observations 254,490R

2 0.276Standard errors in parentheses⇤ p < 0.05, ⇤⇤ p < 0.01, ⇤⇤⇤ p < 0.001 44

them to spend more while at the store (Venkatesan and Farris, 2012). This suggests

that returns to reward coupons should be increasing with successive campaigns. Even

if the average e↵ect is lower, if personalized reward campaigns exhibit increasing

returns, there may be reason to believe the average e↵ect may be higher in the long-

run.

Figure 8 depicts the marginal e↵ect of personalized reward and targeted promo-

tions for successive campaigns. Each campaign type has a similar e↵ect when it is

the first campaign received by households of its type. However, the e↵ect begins to

diverge as households receive more campaigns. Personalized reward campaigns begin

to drop in e↵ectiveness, even yielding negative marginal impact on sales by the fourth

and fifth campaigns received.

Figure 8: Average Iterative E↵ect on Expected Sales

One potential explanation for the decreasing returns to personalized reward coupons

is that the campaigns rely on surprise and delight to drive engender a feeling a grati-

tude from customers. If this is the case, as households receive more reward campaigns,

the impact of the campaigns may diminish as seen here. These results are robust and

45

do not su↵er from small sample size problems (144 households received 4 personalized

reward campaigns and 137 households received all 5 Reward campaigns). These re-

sults seem to suggest that the short-term surprise eliciting positive response in earlier

campaigns wears o↵ as households receive multiple campaigns, suggesting that sur-

prise is the main driver a↵ecting the increase in sales. If appreciation compounded,

then one would expect that the marginal impact of successive campaigns would in-

crease.

Targeted promotion campaigns, on the other hand, maintain and increase e↵ec-

tiveness as households receive more campaigns. Since targeted promotion campaigns

have both reward and acquisition characteristics, the purpose of these coupons is to

redirect attention to products less frequently purchased by signaling their relevance by

simultaneously o↵ering discounts for products frequently purchased. The informative

aspect of these coupons yield a persistent impact on expected sales.

While both targeting techniques utilize price discounts for products relevant to the

household, the mechanism through which each elicits consumer response are di↵erent.

Identifying the iterative impact of campaigns enables us to track the compounding

impact of the campaigns and gain more insight into why each campaign type a↵ects

consumer behavior.

7.2.3 Decomposing Impact on Coming to Store and Spending Conditionalon Coming

Expected sales conflate both the likelihood of coming to the store and spending condi-

tional on coming to the store. In order to better understand the mechanisms through

which each campaign a↵ects spending, I analyze both the impact on both drivers of

expected sales. Because the coupons a↵ect both components, I control for selection in

coming to the store when estimating the e↵ect of the targeted coupons on sales within

46

Table 17: Average Iterative E↵ect on Expected Sales

Weekly Sales Weekly Sales� 1 Personalized Reward 5.88⇤⇤⇤ 3.85⇤⇤⇤

(0.49) (0.32)� 2 Personalized Reward 4.70⇤⇤⇤

(0.55)� 3 Personalized Reward 3.66⇤⇤⇤

(0.63)� 4 Personalized Reward �4.16⇤⇤⇤

(1.10)� 5 Personalized Reward �7.30⇤⇤⇤

(1.53)� 1 Targeted Promotion 13.97⇤⇤⇤ 7.43⇤⇤⇤

(0.41) (0.63)� 2 Targeted Promotion 6.88⇤⇤⇤

(0.78)� 3 Targeted Promotion 6.32⇤⇤⇤

(0.94)� 4 Targeted Promotion 8.03⇤⇤⇤

(1.17)� 5 Targeted Promotion 9.21⇤⇤⇤

(1.42)� 6 Targeted Promotion 14.24⇤⇤⇤

(1.95)� 7 Targeted Promotion 7.01⇤⇤

(2.68)� 8 Targeted Promotion 15.40⇤⇤⇤

(3.73)� 9 Targeted Promotion 27.88⇤⇤⇤

(5.78)Constant 13.60⇤⇤⇤ 13.80⇤⇤⇤

(0.34) (0.34)Observations 254,490 254,490R

2 0.28 0.29Standard errors in parentheses⇤ p < 0.05, ⇤⇤ p < 0.01, ⇤⇤⇤ p < 0.001

Due to space constraints, control function coe�cients are not displayed.

Small samples for 10 or more campaigns yield insignificant estimates.

47

the store. Using the Heckit approach, I first estimate the probability of coming to

the store, and then use the estimated likelihood in the estimation of sales conditional

on coming. Results are shown in Table 18.

Both personalized reward and targeted promotion coupons increase the likelihood

that a household will come to the store in the weeks of the campaign. However,

the marginal e↵ect induced by targeted promotions on likelihood is twice as high as

the marginal increase in likelihood caused by personalized reward coupons. Thus,

households increase their probability of coming to the store more when they receive

targeted promotion coupons. This is an important driver of increased expected sales

above, particularly because the households targeted by either campaigns spend more

on average when they come to the store than households that are not sent coupons

(Tables 8 and 9).

Once in the store, redemptions of both personalized reward and targeted promo-

tion coupons have a significant impact on lifting sales, with a higher impact with

personal reward coupons. The increase in both redemptions is substantial relative

to the average size of the coupon discounts shown in Table 2. It has been shown by

Heilman, Nakamoto, and Rao (2002) that in-store discounts increase sales and this

is corroborated with the increase in sales from store discounts like retail discounts,

other coupons and retailer match discounts, redemptions of both personal reward and

targeted promotion coupons have a considerably higher impact on sales. The higher

impact of reward redemptions may reflect stronger positive feelings from being per-

sonally rewarded for products the household prefers. One caveat regarding the impact

of the redemption e↵ect is that redemption rates are relatively low (as seen in Table

3), so this jump in sales does not happen every time a household receives a coupon.

Finally, I find a positive spillover e↵ect of targeted promotion campaigns equal

to $1.86 and a negative spillover of personalized rewards of �$2.04. The spillover

e↵ect is the change in sales caused by the campaigns, apart from redemptions. This

48

Table 18: Heckman Selection Regression

P (came = 1) E(wksales|came = 1)Personalized Reward 0.06⇤⇤⇤ �2.04⇤⇤⇤

(0.004) (0.33)Targeted Promotion 0.12⇤⇤⇤ 1.86⇤⇤⇤

(0.005) (0.40)Reward Redemption 9.57⇤⇤⇤

(1.37)Promotion Redemption 7.20⇤⇤⇤

(2.32)Retail Discount 3.24⇤⇤⇤

(0.01)Other Coupon Discount 0.75⇤⇤⇤

(0.09)Match Coupon Discount 1.71⇤⇤⇤

(0.39)Constant 47.90⇤⇤⇤

(0.92)Observations 254,490 130,678Standard errors in parentheses⇤ p < 0.05, ⇤⇤ p < 0.01, ⇤⇤⇤ p < 0.001

Due to space constraints, control function coe�cients are not displayed.

Dependent variable is weekly sales; marginal e↵ects are shown.

show that targeted promotion campaigns increase sales within the store even when

households do not redeem the products. Personalized reward campaigns, on the other

hand, have lower sales during campaigns when households do not redeem coupons.

Venkatesan and Farris (2012) similarly find a redemption e↵ect and spillover e↵ect

on sales (which they term exposure e↵ect).

7.2.4 Department Sales

From the in-store sales, we know that targeted promotion campaigns induce additional

spending while customers are in the store apart from redemption spending. I call this

49

the spillover e↵ects of the targeted promotion coupons.

One mechanism through which targeted promotion campaigns could increase spend-

ing once households are in the store is through encouraging customers to explore the

department advertised in the coupon. When considering the purchase of the adver-

tised product, the customer moves out of her typical grocery path within the store,

opening the opportunity for her to examine nearby products. Using the same Heck-

man approach above, I examine the spillover e↵ect of targeted promotion campaigns

within the departments of the couponed products. The results are shown in Table 19.

Controlling for redemption and other discount e↵ects, I find that targeted promotion

campaigns increase sales by $2.92 on average.

This positive spillover e↵ect helps explain the mechanism through which targeted

promotion coupons drive overall sales at the store. By conveying information relevant

to the households, they encourage customers to consider products promoted through

the campaigns. While customers may not purchase the specific products in the cam-

paign, they encourage customers to examine similar products, increasing purchases

in that department of the store.

The results coincide with Hui et al., (2013) who find that unplanned purchases

increase the longer a customer is in the store and number of interactions with the

product area. By increasing the likelihood that a customer moves to a section of the

store, the targeted promotion coupons increase likelihood of unplanned spending in

the proximity of the product. Similar results were found in the online setting by Dias

et al. (2008).

One limitation of this result is that I do not observe the personalized reward

coupons at the product or department level. The retailer has made available the

pool of coupons available to personalized reward recipients, but has not identified

the specific coupons each personalized reward recipient received. However, while this

variable is missing, the results above suggest that there is limited reason to believe

50

Table 19: Department Sales Controlling for Heckman Selection Regression

E(wksales|came = 1)Dept Targeted Promotion 2.92⇤⇤⇤

(0.025)Dept Redemption 7.68⇤⇤⇤

(0.288)Dept Retail Discount 2.88⇤⇤⇤

(0.002)Dept Other Coupon Discount 0.68⇤⇤⇤

(0.018)Dept Match Coupon Discount 5.39⇤⇤⇤

(0.060)Constant 0.30⇤⇤⇤

(0.026)Observations 8,763,228Standard errors in parentheses⇤ p < 0.05, ⇤⇤ p < 0.01, ⇤⇤⇤ p < 0.001

Due to space constraints, control function coe�cients are not displayed.

Dependent variable is weekly sales; first stage results are identical to Table 18.

that personalized rewards have a spillover e↵ect at the department level since they

do not have an e↵ect at the store level.

8 Conclusion

As shopping transitions from brick and mortar stores to online, mobile, and hybrid

environments, stores are increasingly tailoring their communication with customers

in order to set themselves apart from the crowd. Data mining and machine learn-

ing algorithms equip firms with greater ability to target advertisements with more

precision. As retailers move toward more targeted advertisements, it is important to

consider the targeting approach that will yield the best return on firms’ investments.

I show that during the weeks of campaigns, targeted promotion coupons increase sales

51

more than personalized reward coupons.

Messages tailored to individual preferences aim to increase advertising e↵ective-

ness and decrease waste relative to mass marketing campaigns. By focusing on prod-

ucts relevant to customers’ revealed tastes, marketers have shown that targeted ad-

vertisements elicit higher response rates than non-targeted advertisements. I find

evidence in the redemption e↵ect that corroborates with these findings.

I also show that an important and often overlooked metric for understanding the

ROI of a targeted advertisement is the spillover e↵ect, or the change in spending

of related products not directly included in the targeted advertisements. By recom-

mending products relevant to the targeted group, but not frequently purchased, the

targeted promotions redirect customers’ attention to products they may purchase.

The informative component of the promotions directs customers to segments of the

store and encourages them to look at products they otherwise would not have con-

sidered. Because of this targeted informative message, the customer is more likely

to purchase products in the promoted department even if she does not redeem the

promoted product.

This has considerable implication for targeted advertising in a broader context.

A lot of attention is placed on retargeting advertisements for products purchased

recently in the past. The findings in this paper suggest that retargeting campaigns,

even when combined with rewards, may have diminishing returns. On the other

hand, targeted promotion advertisements in the form of recommendations may have

a bigger impact on overall sales by introducing spillover e↵ects.

This research is most relevant for retailers with multiple products. For example,

if a clothing retailer observes a customer regularly purchases clothing of a certain

style, a reward campaign would send a discount to that customer encouraging her to

purchase more of the same type of clothes. A targeted promotion campaign would

see the pattern and suggest a product in a complementary or related style that the

52

customer had not purchased in the past. This research suggests that the first type

of targeting would encourage higher redemption of the product rewarded, but the

latter type might encourage the customer to increase purchases in the related style,

increasing sales more overall.

The lessons can also extend to a mobile advertising context, but the incentives

of the mobile advertiser need to be aligned so that they are able to recoup some of

the spillover benefits. A way this may work is if the mobile advertisers promotes a

network of retailers in the same proximity. While the goal is currently to sharpen

geo-targeting capacity, once the capacity to identify the frequented locations matures

mobile advertisers will need to assess the best way to target potential customers. A

reward or retargeting approach would focus on sending advertisements or discounts

for restaurants or retailers frequently visited. A targeted promotion approach would

send advertisements for stores a little outside the normal path taken by the individual.

Targeted advertisements have the capacity to personalize retailer and customer

relations. This research suggests stores can make this more e↵ective by sending tar-

geted promotions rather than rewards. I have investigated the direct impact during

the campaigns. There is room for more research in assessing how each type of targeted

campaign a↵ects long-term loyalty. Additionally, it would be good to test how the

price sensitivity of customers change with each type of targeted campaign. Specifi-

cally, it would be interesting to know the e↵ect of the coupons on the price sensitivity

of rewarded versus promoted products and the other products in the department.

There is room for much more research in comparing the e↵ectiveness of di↵erent

types of targeted campaigns.

53

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