Estimating the Impact of Sales Representatives On the Average

Revenue Per Customer∗

Alon Eizenberg

Department of Economics, the Hebrew University of Jerusalem, and CEPR

December 2015

Abstract

Companies make extensive use of sales representatives with the aim of improving their abilityto capture value from customers. This paper uses a unique transaction-level dataset from a com-pany in the High Technology sector to study the impact of interaction with sales representatives onthe Average Revenue Per Paying User (ARPPU). A major challenge is that the interaction may becorrelated with the customer’s unobserved, inherent willingness-to-pay for the product. The data,however, provide a natural exclusion restriction, motivating an instrumental variable strategy. De-scriptive analysis indicates that interaction with a representative results in a higher incidence oflonger subscription periods, consistent with institutional details confirmed by the company. Fur-thermore, naive regression results imply that interaction with representatives substantially increasesthe mean revenue. These findings, however, are not robust to endogeneity concerns, and, indeed,instrumental variable estimates indicate that interaction actually lowers the mean revenue. An ad-ditional finding is that interaction with a sales representative has a negative effect on the probabilityof repeat purchase, conditional on an initial transaction taking place. These results are consistentwith sales representatives drawing low-WTP customers into the customer pool. Sales representatives,therefore, have nontrivial effects on the distribution of preferences among the company’s customers,thus improving its ability to price discriminate.

Keywords: Resource Allocation, Marketing, Sales force.

∗Contact: alon.eizenberg@mail.huji.ac.il. I am grateful to a company in the High Technology sector for making theirdata available, and for their helpful feedback. I thank Itai Ater and Saul Lach for helpful comments. Orry Kaz providedexcellent research assistance. This work was supported by the Israeli Science Foundation (ISF) Grant #1338.

1 Introduction

Consider a firm that offers a product (or a menu of differentiated products) for sale using two

parallel channels. The first channel operates online: the firm’s products are available for direct

purchase on its website by filling out a simple online form. This channel does not involve

interaction with a sales representative. In the second channel, in contrast, the customer interacts

with a sales representative (e.g., by phone, e-mail or an online chat) in order to learn more

about the company’s offerings, and, potentially, make a purchase. This paper asks the following

question: what is the impact of such direct communication on customers’ willingness-to-pay

(hereafter WTP) and the company’s revenues? And, to what extent does such communication

enhance the company’s ability to effectively price discriminate? While many companies make

substantial investments in call centers and in the training of sales representatives, empirical

evidence on the effectiveness of this investment is mixed. This paper wishes to contribute to the

empirical literature on this issue by studying sales representatives’ effect on revenue extraction

and client retention within a unique dataset in which transactions are observed to take place

with and without communication with a sales representative.

Similarly as with advertising, direct communication (hereafter DC) between sales representa-

tives and customers may have two main roles: informative (i.e., informing consumers of important

aspects of the product), and persuasive (i.e., convincing the customer to make a purchase, or to

choose a more advanced and expensive version of the product). This paper does not attempt to

separate out the two effects, but rather aims at documenting the existence and magnitude of the

total effect of DC, leaving its interpretation for future work.

An important aspect of DC is that it creates a bi-directional information flow between the

customer and the company. While the customer learns about the company’s offerings, the sales

representative can use the communication to learn about the customer’s preferences and needs.

Such information could be used to offer the customer a product that best fits their needs, or

to effectively price-discriminate. For instance, the sales representative may tailor a discount or

promotional offer based on her perception of the customer’s price sensitivity.

The literature in economics and marketing has long recognized the important role played by

such information flows (Jayachandran et al. 2006). Firms have also been increasingly aware

of this issue, as evident by the large number of businesses that invest in Customer Relation-

ship Management (CRM) technology, i.e., software that facilitates the process of collecting and

analyzing customer-specific information. Such information allows the firm to identify the het-

erogeneous characteristics of its clients and to discriminate among them (Peppers et al. 1999).

Implementing such an approach is often a complex and costly process, and evidence on its suc-

cess are mixed: according to a study by the Gartner Group, 55% of all CRM projects fail to

produce results (Rigby et al. 2002). This paper adds to an empirical literature that attempts

2

to shed light on the effectiveness and impact of sales representatives, with the goal of improving

our understanding of the impact of such investments.

This paper measures the effectiveness of direct communication with customers by utilizing

a unique dataset from a company that operates in the High Technology sector (hereafter “the

Company”). The Company sells SaaS (software as a service) solutions to business customers.

It targets two main customer segments: large (”Enterprise”) customers, and Small and Medium

Business (”SMB”) customers. The empirical analysis in this paper focuses on SMB transactions.1

SMB customers visiting the Company’s website observed a menu of vertically-differentiated

product versions. These customers could then choose their preferred product, and to complete

the transaction online, without communicating with a representative. Alternatively, customers

could communicate with a sales representative. This paper offers an econometric analysis of the

effect of DC on such transactions.

The data allow me to track an individual customer over time, and, indeed, repeated customer

purchases are an important aspect of the data. I therefore define the individual customer, and

not the individual transaction, as the unit of analysis throughout most of the paper. I conduct

regression analysis that investigates the impact of DC on the conditional expectation of consumer

spending. Since the data cover all transactions involving positive payments to the company, this

approach effectively focuses the analysis on the impact of DC on the Average Revenue Per Paying

User (ARPPU).

The ARPPU statistic, and the closely related term ARPU (Average Revenue Per User) are

widely used by practitioners to evaluate company performance. While the term ARPU may take

into account free users, the ARPPU explicitly ignores them and computes the average revenue

over paying users only. The ARPPU is often used in the context of SaaS and related industries.2

It is, therefore, of interest to investigate the manner with which the ARPPU it is affected by

interacting with a sales representative.

Descriptive analysis reveals that DC is associated with a higher incidence of long subscrip-

tion periods, consistent with institutional details confirmed by the Company: specifically, sales

representatives received incentives to stir customers into longer subscription periods, and were

allowed to use targeted promotional discounts for that purpose. Furthermore, a naive regression

1“Enterprise” customers are handled via a specialized sales team, and tend to purchase rather expensive (and sometimestailor-made) versions of the Company’s product. Of note, despite the labeling of the online channel as targeting “SMB”clients, in practice there was no effective mechanism that limited the access of large customers to online purchasing viathis channel.

2To provide a few examples: In a May 2014 story, Forbes.com reports that “...the average revenueper paying user in (the mobile gaming market in) China increased from $26.72 in Q1 2013 to $32.46in Q1 2014. In comparison, ARPPU in the U.S. improved from $19.52 to $21.60 during the same pe-riod” (http://www.forbes.com/sites/greatspeculations/2014/05/13/chinese-carriers-to-team-up-again-this-time-in-mobile-gaming/). Chargebee.com reviews “SaaS metrics” and notes that “...Many VC funded startups do not take freemiumplans into consideration while calculating ARPU. Hence the assumption that ARPU is only for paying customers”(https://www.chargebee.com/blog/decoding-saas-metrics-arpu/). The Google Play webpage allows Android developersto track their application’s ARPPU.

3

analysis demonstrates that DC has a positive, statistically as well as economically significant ef-

fect on the revenue extracted from the customer over the sample period. While these findings are

instructive, they cannot credibly reveal a causal effect of DC on transaction values and revenue.

The reason is that DC interaction is inherently endogenous. First, contact with representa-

tives could be endogenously initiated by customers. If customers who were a-priori willing to pay

higher prices for the Company’s product also displayed an above-average tendency to initiate DC

(say, as a reflection of their heightened interest in the product), the positive relationship could

be spurious rather than causal, leading us to overestimate the effectiveness of DC in extracting

consumer surplus. Second, contact with potential (or actual) clients can also be endogenously

initiated by the sales team. If sales representatives systematically contact customers they per-

ceive, based on factors unobserved to us, to have a high WTP, this would further reinforce the

positive bias described above. If, on the other hand, sales representatives systematically target

low-WTP clients who are “on the fence” with respect to making a purchase, this would generate

a negative bias in the estimation of the effect of DC. While the overall direction of the bias is,

therefore, not a-priori clear, it clearly suggests that we need an instrumental variable strategy to

ameliorate the bias.

Of note, this issue suggests an interesting parallel with the traditional endogeneity problem

encountered in the study of the impact of advertising on sales (e.g. Schmalensee 1972, Berndt

1991, Bagwell 2003, Elberse and Anand 2005). In that literature, advertising is endogenous since

it is correlated with unobserved demand shifters at the product level. In Berndt, Pindyck and

Azoulay (2000), firm spending on sales representatives’ activity is, again, correlated with unob-

served shifters at the product level (more on this below). In the current context, the interaction

with a sales representative is endogenous since it is correlated with unobserved characteristics at

the level of the individual customer.

The current paper contributes to the literature in two ways: first, it uses a unique dataset

that allows one to observe, regarding each transaction, whether or not it involved communication

with a sales representative. Such information is often not available to researchers (e.g., typical

data from a retailer would often not report whether or not a customer shopping for clothes was

assisted by a representative). Second, within this particular context, an instrumental variable

strategy is suggested to overcome the endogeneity problem.

The identification strategy is based on the fact that transactions are characterized by a “mar-

keting channel” that is observed in the data. The marketing channel captures the manner by

which the customer arrived at the website (for example, via a search engine, by clicking on a

paid link, or via an affiliate website). Of note, the marketing channel information is missing

for a relatively large group of customers, and in Section 2 below, I provide institutional details

that support the notion that variation in the channel information (including, whether or not it

has been recorded) stems from technical issues that can reasonably be viewed as exogenous to

4

unobserved shifters of customers’ WTP. Indeed, my identifying assumption is that the technical

information embedded in the marketing channel is not correlated with the customer’s unobserved

WTP.

The marketing channel, on the other hand, is correlated with the endogenous variable: a

dummy variable that takes the value 1 if direct communication with a sales representative takes

place. This notion is based on both statistical evidence, and on institutional details. Specif-

ically, one of the channels via which communication could be established was that potential

customers often visited the website, and, via the website, downloaded a free trial of the software,

requested a demo, or asked to be contacted by a representative. This initial visit caused the

customers’ details, including the marketing channel, to be recorded. Sales representatives would

then contact such customers with the goal of making a sale. This created a correlation between

the observed marketing channel and the endogenous DC communication variable, making the

marketing channel an effective instrumental variable.

In contrast to the naive regression results, the instrumental variable estimates suggest a neg-

ative and significant impact of DC on the revenue extracted from a customer, and this result is

found to be robust across various specifications and sensitivity analyses. The overall direction of

the bias embedded in the OLS results, which was a-priori ambiguous, was therefore positive.

Interaction with a sales representative, therefore, decreases the average revenue per paying

customer. I interpret this finding as follows: sales representatives have two systematic effects

on customer purchase decisions. First, they may increase the extracted revenue by inducing

customers to purchase more advanced versions of the product, to engage in repeat purchases, or

to purchase longer subscription periods. This positive effect on revenues is mitigated by the use

of promotional discounts by the representatives, but may still be considered positive nonetheless.

Second, sales representatives may systematically draw into the customer pool customers who

would otherwise refrain from purchasing any of the firm’s offerings. Since these customers are

likely to a-priori display a low willingness to pay for the product (e.g., they may have simply

downloaded a free version without a real intent of making a purchase), they are likely to purchase

cheap versions of the product, even after their interaction with the sales representative. The

instrumental variable results suggest that the second effect is strong enough to offset the first,

creating an overall negative effect of DC on the average revenue. The naive regression that does

not correct for endogeneity delivers the opposite result since it fails to account for the fact that

some customers may choose to talk to a representative because of an unobserved high valuation

for the product, which may exaggerate the first effect, causing an upward bias in the estimated

coefficient on DC interaction.3

These findings indicate a nuanced effect of sales representatives on firm performance: while

3Of note, while the evidence is consistent with the interpretation provided above (i.e., with the notion that the negativeimpact stems from wooing low-WTP customers), it is difficult to demonstrate this interpretation directly given the datalimitations (specifically, the lack of data regarding customers who end up not making a purchase).

5

they can expand the company’s customer base, this expansion results in a lower ARPPU measure.

Since both customer base and mean revenues are closely tracked by analysts and are often

referred to in discussions of a firm’s market value, these findings shed light on the impact of sales

representatives on firm performance: it likely expands the customer base at the cost of reducing

the per-customer average revenue. This insight may be important for managerial decisions aimed

at improving specific aspects of firm performance. Another implication of this finding is that sales

representatives increase the dispersion of willingness-to-pay in the firm’s customer population,

reflecting expanded opportunities for price discrimination across various consumer segments.

This nuanced view is further reinforced by an additional finding from the empirical analysis:

conditional on an initial transaction taking place, interaction with a sales representative lowers

the probability of repeat purchase by the customer. Once again, this result can be interpreted

in light of the likely role of sales representatives in wooing low-WTP costumers to perform a

transaction.

The rest of the paper is organized as follows. Following a brief literature review, Section 2

describes the data used in this research, and institutional details that pertain to the Company.

Section 3 presents estimation results, and Section 4 offers concluding remarks.

Literature. A large body of literature is dedicated to the study of salesforce effectiveness.

Rich et al. (1999) tie this interest to the “...obvious link between sales performance and overall

corporate revenue,” and explain that studying the effectiveness of the salesforce can be a useful

shortcut to understand many aspects of corporate success. Jackson et al. (1995) note that

managers use a variety of strategies to evaluate salesforce performance, reporting that managers

display a “continued reliance on qualitative measures” (such as the salesperson’s attitude or

professional knowledge) alongside more quantitative indicators at the salesperson level, involving

both output measures (number of accounts, sales and profits) and input measures such as the

number of calls made (see also Kuster and Canales 2008).

As Rich et al. further describe, consistently with the mix of measures used by managers,

the sales management literature has also measured salesforce performance using a variety of

research strategies, with roughly half the studies using subjective evaluations from managers,

and the other half using objective data on indices such as sales volume, sales commissions or

percent of quota.4 Albers, Murali and Sridhar (2010) provide a meta-analysis of 46 empirical

studies of sales representatives’ effect on sales output, and report a weighted mean elasticity of

about 0.34 (implying that a 1% increase in spending on this activity increases total sales by

0.34%). Among the contributions to this line of literature is Horsky and Nelson’s (1996) analysis

of the relationship between district level sales and salesforce size. These authors estimate this

relationship using data from two firms, and conclude that the national size and district allocations

of the salesforce were determined by those firms in a reasonably effective fashion. Narayanan,

4This observation derives from meta-analysis in Churchill et al. (1985).

6

Desiraju and Chintagunta (2004) study the impact on revenue of marketing mix variables in

the Pharmaceutical industry and find synergies between direct to consumer (DTC) advertising

and detailing (sales representatives’ visits at physicians’ offices). Consistent with some previous

findings in the literature, they also find that detailing is associated with a higher return on

investment than DTC. Berndt, Pindyck and Azoulay (2000), also looking at the Pharmaceutical

industry, estimate the response of equilibrium market shares to marketing levels, namely, the

depreciated stock of detailing minutes. Explicitly acknowledging that marketing levels are likely

to be endogenous, they use a set of instruments including the log of the wage rate in the industry,

and the cumulative stocks of detailing minutes spent on other products of the same firm.

The current paper, therefore, joins an established literature that estimates the response of

sales to salesforce utilization. In this literature, measures of sales or revenue serve as dependent

variables, while measures of the intensity of salesforce utilization (typically captured by the

expenditure on this activity, by salesforce size or similar measures) serve as the key independent

variables. The current paper follows a different research design, treating the individual customer

as the unit of observation, and studying the average revenue from paying customers as a function

of whether they interacted with a sales person, or purchased via an “anonymous” online channel

absent such interaction. To the best of my knowledge, these features of the research design

differentiate this paper from previous work in this area, and provide a different perspective on

the revenue impact of the salesforce. Within this specific setup, and its institutional details, I offer

an identification strategy that overcomes the endogeneity of communicating with a sales person.

This links my work to other papers that explicitly account for the endogeneity of marketing

efforts, such as Berndt, Pindyck and Azoulay (ibid.) or Chintagunta (2001) who considers the

endogeneity of marketing variables (that may include price, promotions, or salesforce activities)

in the estimation of discrete choice models of demand. As discussed above, the endogeneity issue

in the current paper is at the level of the individual customer, whereas in the cited papers, it is

present at the product level.

In addition to the literature that measures the impact of the salesforce on revenues, discussed

above, a large literature focuses on a closely-related question: how can firms design optimal sales

force compensation schemes? John and Weitz (1985) provide a survey and state five factors that

should be considered in determining the role of salary vs. incentives in an optimal compensation

scheme, one of which is the partial derivative of the sales response curve with respect to selling

effort. Empirical measurement of the impact of sales representatives on sales is, therefore, an

important step toward designing effective compensation strategies.5 In a theoretical contribution,

5The empirical literature on salesforce compensation offers many additional contributions. Some recent examples includeMisra and Nair (2011) and Chung, Steenburgh and Sudhir (2014) who estimate structural dynamic models to shed light onthe role played by quotas and bonuses on salesforce effectiveness in generating sales. Kishore et al. (2013) conduct a largefield study at a pharmaceutical firm in which quota-based bonuses were replaced by equivalent commissions, resulting insubstantial improvement in extracted revenues.

7

Simester and Zhang (2014) consider the tension that arises, in business-to-business settings,

between the company’s salesforce, that lobbies for lower prices, and pricing managers, who

prefer to protect profit margins. The paper arrives at the conclusion that, in equilibrium, the

lobbying serves the role of eliciting information from the salesforce regarding the demand state.

This reflects the complexity of the relationship between salesforce actions and revenue. While

my analysis does not address the strategic issues considered by these authors, its main finding

— the negative impact of sales teams on the average revenue — is related to the mechanisms

they consider.

This paper is also related to a strand of the empirical literature in economics that attempts

to measure customer heterogeneity, reflected in preferences and willingness-to-pay (e.g., Berry,

Carnall, and Spiller 1996). It is also related to a vast literature on price discrimination (e.g.,

Verboven 1996, Clerides 2002, Clerides 2004, Leslie (2004), Cohen 2008, Borzekowski, Thomadsen

and Taragin 2009).

2 Data and institutional details

2.1 Transactions, customers and prices

The data cover the universe of 12,218 SMB transactions performed during eight months, January

through August 2012. For each such transaction, we observe the package chosen (out of three

vertically-differentiated packages), the length of the subscription (ranging from one month to

twelve months), and the total amount paid. Crucially, we also observe whether the transaction

involved direct communication with a sales team, or, alternatively, was performed online. DC

occurs in 19.9% of all transactions.6

Each of the 12,218 observed transactions can be characterized along two dimensions. The first

is the quality of the package chosen: the Company offered three levels of service, to which I refer

as A, B and C (with A offering the highest value). More advanced packages are characterized

by increased functionality of the offered services. The second dimension is the duration of the

subscription to the services: one, three, six or twelve months.7

An important aspect of the data are repeated transactions by individual clients. Since each

transaction is associated with a client ID code, it is possible to track an individual customer’s

transactions over the eight sample months. The 12,218 transactions were generated by 3,550

unique clients, implying an average of 3.44 transactions per customer during the studied period.

The data do not allow us to observe transactions that took place before or after the eight sample

6A caveat is that, if a customer conversed with a sales team, but did not complete a transaction, and later performedthe transaction online creating a new ID, the analysis is likely to miss the fact that DC occurred. Unfortunately, the datado not allow me to address this issue, and it is also difficult to gain a qualitative insight into its prevalence.

7The data also contain very few transactions that do not fall into this classification. Six transactions are missing theplan description information, and an additional 268 transactions belong in two other small plans.

8

months (though subscriptions purchased prior to the sample period could clearly still be valid

during it, and, similarly, observed subscriptions purchased during the sample period could be

valid beyond it). The repeated transaction aspect of the data motivates the focus on the total

amount of a client’s transactions during the sample period as the dependent variable of interest.

The mean of these values across customers is the company’s ARPPU over the eight months

(from SMB transactions), and this study aims at identifying how this mean varies conditional

on contact with a sales representative.

Also motivating this focus is the fact that “DC status” (i.e., whether communication with a

representative takes place or not) appears to be remarkably stable over different transactions of

an individual customer. Of the 3,550 unique customers, only 35 (i.e., about 1%) of customers

displayed a switch in the sense of performing transactions both with and without DC. All other

customers either always communicate with a representative, or never do so. This fact makes

it reasonable to treat DC status as fixed at the level of the individual customer. For internal

consistency, the 35 customers who switched were removed from the analysis.

Plans and pricing. Each combination of quality and duration (e.g., A3 which stands for a

three-month subscription to the “A” package) is observed to be sold at different prices, reflecting

promotions, and, possibly, the ability of the sales team to tailor a transaction to the WTP of a

specific customer with whom they communicate. Table 1 describes the “modal” (in the sense of

being most often observed) price for each quality-duration pair:

Table 1: ”Modal Pricing,” US$

Quality/duration 1 3 6 12

A 990 2,000 4,950 7,900B 290 600 1,450 2,600C 99 240 495 890

Negligible plans omitted.

The nonlinearity of the modal price in the subscription length is evident: a three-month

subscription, for example, costs less than three times as much as a one-month subscription in

either the A, B or C packages. This is consistent with second-degree price discrimination.

“Non-modal” prices are present in some 766 transactions, or 6.3% of the total 12,218 trans-

actions. In 92 such transactions, price is lower than the modal price due to reimbursement for

a previous subscription that has not been fully exploited. This was verified by calculating the

discount, subtracting it from the modal price, and checking that this matches the actual price

paid. For example, when we observe that a C1 transaction with the same client took place 27

days prior to purchasing another subscription, the refund for the four non-utilized days should

be (4/31)*99.8 Some 184 additional “non-modal” cases are clear promotional discounts that are

8Since the transaction time within the day is not recorded, I considered the bounds of (3/31)*99 and (5/31)*99 in such

9

recurring (e.g., a 20$ discount over the modal price).

This leaves 766-92-184=490 cases where no clear explanation is available for the non-modal

price. Some of these cases likely involve refunds for previous transactions that we do not observe

(since they occurred prior to our sample period). Other cases may involve various promotions,

and, finally, some of these values may be mistakes. The non-modal pricing is not more common

in transactions that involve DC, such transactions comprising 46% of the total non-modal cases

(with the remaining 54% cases pertaining to online transactions). Such prices cannot, therefore,

be systematically attributed to negotiations with sales representatives over discounts. Following

discussions with the Company, I determined that certain transaction prices are clear mistakes,

and so I removed them from the regressions reported in the results section below (as explained

in detail there). Non-modal prices are more frequent with regard to expensive transactions

(e.g., purchases of the A package, or of 12-month subscriptions). For sensitivity checks, I also

performed regressions in which all observations involving non-modal prices were removed from

the sample, delivering similar results.9

Insights into the research question can be obtained by examining the distribution of transac-

tions over the 12 plans, conditioning on whether the transaction involved contact with a sales

team, or not. Table 2 reports the frequency of each plan within transactions involving such com-

munication (top panel), and transactions not involving it (bottom panel), using the full sample

of 12,218 transactions (again excluding certain negligible plans). The table conveys first that,

regardless of DC status, plans B1 and C1 are by far the most popular. It further indicates a

likely role for the sales team in directing customers into more expensive plans, and, in particular,

ones involving a longer subscription period. Specifically, within a plan type (i.e., A, B or C)

the fraction of transactions characterized by a one-month subscription is substantially higher for

transactions not involving direct communication with a sales representative. Concretely, as the

table shows, this fraction is 87.3%, 95.2% and 97.2% for A, B and C plans purchased without

communication, respectively, whereas in transactions involving communication, these fractions

are 34.2%, 72.2% and 88.9%, respectively.

These descriptive patterns are consistent with institutional details. As confirmed by the Com-

pany, sales representatives were provided incentives to try and divert potential customers into

longer subscription periods, and were able to use promotional discounts to achieve this goal. This

fact bears several implications for the role played by the sales representatives. The mere fact

that transactions involving DC resulted in longer subscriptions does not, in itself, imply a causal

effect. Part of this effect could be spurious, in the sense that customers who, to begin with, were

more likely to purchase longer subscriptions may have also been more prone to communicate

with a representative. The formal regression analysis provided in the section below addresses

cases. The 92 cases alluded to above are those in which the discount fell in between the bounds.9These additional regressions are available from the author upon request.

10

this endogeneity problem via an instrumental variable strategy, allowing me to identify a causal

effect. Second, to the extent that representatives are able to stir customers toward longer sub-

scriptions, they may be achieving this goal via promotional discounts, thus offsetting some of the

positive effect that interaction with representatives has on the extracted revenue. The formal

analysis below considers the overall revenue from a customer as a dependent variable in order to

take this issue into account.

Table 2: Plans purchased conditioning on contact with sales team

Transactions with DC=1

Quality/duration 1 3 6 12 Total % one-month

A 26 13 16 21 76 34.2B 854 172 53 104 1,183 72.2C 1,024 68 27 33 1,152 88.9

Transactions with DC=0

Quality/duration 1 3 6 12 Total % one-month

A 89 0 8 5 102 87.3B 3,067 25 77 53 3,222 95.2C 6,037 33 80 59 6,209 97.2

Negligible plans omitted.

2.2 Marketing channels and the exclusion restriction

Also observed in the data are variables that capture different marketing channels that describe

the way in which the customer arrived at the company’s website at the point of initial con-

tact. Those include “Paid” (customer arrived at the company’s website via a paid link or ad),

“Direct” (customer arrived directly at website), “Affiliate” / “Referral” (customer was referred

from another website via an affiliation agreement), “Organic” (customer arrived via a search en-

gine) and “Campaigns.” The marketing channel was collected automatically by the website and

recorded within the Company’s information systems. Of note, this information is not available

for about half of the transactions, for reasons that, as I describe below, can be reasonably viewed

as random.

Table 3 presents the number of transactions in each of these channels and indicates that the

two most prominent channels are “Direct” and “Organic.” Importantly, these marketing channel

variables pertain to the first value recorded for the customer. Therefore, they remain stable over

the various transactions performed by an individual customer. The data also report a “latest

11

marketing” variable which is typically, but not always, identical to the “first marketing” variable

that is used in the analysis and discussed above.

Table 3: Marketing Channels

Marketing Channel # transactions Transaction value mean ($) Transaction value sd ($)

Affiliate 211 361.66 636.56Campaigns 45 608.90 1608.16Direct 3213 256.70 400.04Organic 1852 250.64 434.00Paid 247 305.47 661.26Referral 518 258.11 308.39Missing 6132 262.75 644.80

The table indicates that about half of the 12,218 transactions have a missing value for the mar-

keting channel variable. Discussions with the Company suggest that three mechanisms were re-

sponsible for missing channel information. First, the technical feature that detected and recorded

the channel at the point of first contact between the customer and the website may not have been

fully active at the beginning of the sample period. This conjecture is supported by quantitative

analysis. Specifically, Table 4 below shows the numbers of unique customers who purchased

online (i.e., not via communication with a representative) and for whom a marketing channel

was missing, by the month in which they performed their first recorded transaction (noting that

the sample period is January-August 2012).

Table 4: Missing channels in online transactions

Month # of unique first-transaction customers with a missing channel Percent of total

Jan-12 858 0.693Feb-12 89 0.072Mar-12 112 0.090Apr-12 64 0.052May-12 40 0.032Jun-12 36 0.029Jul-12 31 0.025Aug-12 8 0.006

Total 1238

Notes: During the sample period, a total of 1,238 unique clients purchased online and yet had missing market-ing channel information. The table reports the breakdown of these 1,238 cases by the month in which the firsttransaction with the customer took place. See text.

12

The table shows that, in the entire sample, a total of 1,238 unique customers who performed

online transactions had a missing marketing channel. The table shows the breakdown of this total

by the month in which the first recorded transaction took place. Since this is the initial point

of contact, it is the point in time where the marketing channel should have been recorded.10

The table is consistent with the temporal pattern described above. The failure to record the

marketing channel was most prominent in the first month of the sample: close to 70% of the

1,238 cases occurred in that month. This failure became less and less prevalent over time, and

toward the end of the sample period, the incidence of missing channels for customers purchasing

online almost disappeared.

A second mechanism that led to missing marketing channel information pertains to the fact

that some customers who made a contact with the website did not make a purchase there.

Instead, they performed other actions such as downloading a free trial of the software, requesting

a demo or asking to be contacted by a representative. This information would then become

available to sales representatives who would contact these customers, based on these “leads,”

with the purpose of making a sale. If successful, the representatives had to manually enter the

client’s information into the billing system, so as to record the sale and activate the customer’s

license.11 In such cases, representatives were expected to enter complete information, including

the marketing channel collected when the customer made her initial contact with the website.

Conversations with the Company, however, suggest that representatives were not given proper

incentives to enter complete information, resulting in the marketing channel often not being

recorded.

Finally, the third mechanism responsible for missing channel information pertains to cases

where customers performed transactions by interacting with the sales team, in a manner that did

not involve documented contact via the website. Such situations would arise when representatives

initiated contact with potential customers directly (e.g., via a ”cold call”), or, if customers

contacted the sales team directly by phone (or, say, at a trade show) rather than via the website.12

Marketing channels as an exclusion restriction. The empirical analysis presented below

utilizes the marketing channels as instruments for an endogenous direct communication (DC)

dummy variable, in an estimation equation that explains the revenue extracted from customers.

To serve as an effective instrument, the marketing channel needs to satisfy two requirements:

it must be correlated with the endogenous variable, while being uncorrelated with unobserved

factors that affect customers’ willingness-to-pay.

Beginning with the first requirement, I first review the institutional details that explain why

10A caveat is that some of these customers may have performed their first transaction before my sample period in amanner that is unobserved to me. Notwithstanding this issue, it does not change the point of this analysis: that thetechnical issue that led to missing channels became less relevant over time.

11The need for this manual entry stemmed from lack of synchronization of some of the Company’s information systems.12Such customers may have visited the website, but not in a way that left a recorded marketing channel that was then

associated with their account.

13

the marketing channel should be correlated with the DC dummy variable. As explained above,

an important source of “leads” that sales teams were able to follow up on was visits of potential

customers at the Company’s website. Such visitors would sometimes download a free trial of

the software, request a demo, or ask to be contacted by a representative. Such actions were

often followed by an attempt by the sales teams to contact these customers with the goal of

making a sale. Since the initial contact with the website caused a marketing channel to be

recorded (except for the cases where the information was not recorded due to a technical reason,

as described above), this pattern generates correlation between a recorded marketing channel

and communication with a representative.

I next turn to quantitative evidence regarding this “first-stage” correlation. Descriptive statis-

tics are consistent with this correlation: 27% of observations with a non-missing marketing

channel involve DC, compared to 12% of observations with a missing channel. This regularity

also holds within each type of plan, as shown in Table 5 below:

Table 5: Marketing channel and DC Communication by Plan

Transaction type % DC With Channel % DC Without Channel

C1 21.4% 8.6%C3 74.6% 50.0%C6 43.9% 13.6%C12 63.0% 24.6%B1 26.9% 14.6%B3 89.3% 78.9%B6 46.6% 28.6%B12 70.4% 62.8%A1 46.2% 10.5%A3 100.0% 0.0%A6 70.0% 64.3%A12 85.7% 78.9%

Negligible plans (described above) omitted.

To provide an initial, descriptive outlook on this relationship, I perform a Probit analysis

which results are provided in Table 6. The dependent variable is the DC dummy variable.

This regression includes all 12,218 transactions. The table clearly shows that each of the dummy

variables for the various marketing channels has a positive and significant effect on the dependent

variable, i.e., they positively affect the probability of DC. The constant term captures the omitted

effect of a missing marketing channel, which is negative. These results are consistent with the

notion that a non-missing channel is associated with a higher probability of communication with

a representative, consistent with the institutional details provided above.

In the formal regression analysis offered below, the unit of observation would be the individual

customer rather than the individual transaction, and the revenue garnered from the customer

14

would be regressed on the DC variable via Two-Stage Least-Squares, using the marketing chan-

nels as instruments.13

Table 6: Probit Analysis

Campaign 0.967***

(0.189)Paid 0.579***

(0.0874)Direct 0.587***

(0.0313)Affiliate 0.793***

(0.0908)Organic 0.524***

(0.0376)Referral 0.504***

(0.0631)Constant -1.164***

(0.0206)

Observations 12,218

Notes: See text. Standard errorsin parentheses, *** p < 0.01, **p < 0.05, * p < 0.1

I next turn to discuss the other requirement from the marketing channels: they need to

be uncorrelated with unobserved shifters of customers’ willingness-to-pay. The identification

argument is that the marketing channel conveys technical information that is not likely to be

correlated with customers’ tastes. I now discuss the merit of this assumption considering both

quantitative and qualitative evidence.

Consideration of the three mechanisms that lead to a “missing” channel, as reviewed above,

provides qualitative support to the notion that this variation is largely random, or at least

uncorrelated with the relevant unobservable factors. The first mechanism pertained to the failure

of the relevant information systems to record the channel, especially in the earlier part of the

sample. This technical issue can reasonably be viewed as random.14 The second mechanism

had to do with less-than-full compliance of sales persons with the task of manually entering

13As reported there, a TSLS analysis is used with a linear first-stage, as opposed to the nonlinear first stage reflectedin the Probit analysis, presented here for descriptive purposes only. This avoids the “forbidden regression” issue raised byAngrist and Pischke (2008).

14As described above, customers whose first contact with the website took place in January or February of 2012 weremuch less likely to have their marketing channel recorded than customers whose first transaction occurred in March orthe later months. These minor differences in the timing of the first contact can be reasonably viewed as uncorrelated withunderlying preferences.

15

the marketing channel information into the billing system, in cases where that information was

available to them from an initial contact the customer made with the Company’s website. Since

representatives were not provided with sufficient incentives or motivation to manually enter the

information when completing the transaction, compliance may have been related to the sales

person’s workload on particular days, or with her individual level of commitment to the issue.

Once again, such compliance issues can reasonably be viewed as uncorrelated with the customer’s

willingness to pay.

Finally, the third mechanism had to do with transactions performed via a sales representative

in a manner that did not involve a documented initial contact via the website (and hence no

marketing channel was recorded). Such cases may involve a “cold call” of a sales representative

to a potential customer, or a call initiated by the customer herself. My identifying assumption

implies that this manner of contact — which leads to a “missing” marketing channel — is uncor-

related with unobserved shifters of WTP. This exclusion restriction is reasonable: for example,

whether a customer used the website to contact the sales team, or made a direct phone call, does

not convey much information regarding their WTP.

Nonetheless, this assumption could fail if, for example, most of the cases that fall under this

third category involve contact initiated by the Company (resp., by the customer) since this may

imply negative (resp., positive) correlation of a missing channel with WTP. This concern would

be mitigated if cases that fall under this third category would be quite evenly split between

contacts initiated by the Company, and those initiated by the customer. Unfortunately, while

the data allow me to observe whether communication with the sales team took place, it does not

reveal who initiated the contact.

More concretely, the potential pitfall for the identification strategy would arise if customers

who contacted the firm via the website (by downloading a free version, requesting a demo or

asking to be contacted by a representative) are systematically different than customers who call

the company directly on the phone (or, from customers who received a “cold call”). While the

former mode of communication results in a recorded marketing channel, the latter mode involves

a “missing” channel. There is no obvious reason to suspect that such modes of communication

convey substantial information regarding willingness-to-pay. At the same time, it is of course

very difficult to test this assumption directly.

Turning to quantitative evidence on the same issue, and examining Table 3 again, note that

among those transactions with a non-missing channel, “Direct” and “Organic” are the most

heavily represented channels, and that the average transaction values in these channels, as well as

in “Referral” and in the “Missing” category, appear similar. This suggests no systematic effect of

the main marketing channels, or of their “missing” status, on transaction values. Several smaller

channels, however, reflect higher mean transaction values: those are “Campaigns,” “Affiliate”

and “Paid.”

16

While these findings support the general notion that the marketing channel may not be cor-

related with unobserved shifters of willingness to pay, they also indicate that special care may

be needed with respect to the “Campaigns,” “Affiliate” and “Paid” channels in this respect. In

the two-stage least squares analyses offered below, I consider different combinations of channel

dummy variables, and show that the results are robust to the choice of channels that are in-

cluded as instruments, and, specifically, to the inclusion of dummy variables for “Campaigns,”

“Affiliate” and “Paid” channels as instruments.

A referee suggested to also examine the mean transaction value for transactions involving

missing vs. non-missing channels conditional on DC status. Conditional on no DC (i.e., an

online transaction), the mean transaction values are $204.6 and $211.0 for transactions with

missing and non-missing channels, respectively. Conditioning on DC (i.e., transactions involving

communication with a representative), in contrast, a gap obtains between the mean value for

transactions with missing and non-missing channels: $680.4 vs. $399.4, respectively. On the

other hand, examination of the median of the transaction value, conditional on DC, reveals that

it is independent of whether the channel is missing. Specifically, for transactions involving DC,

the median transaction value is $290 when the channel is missing, and again exactly $290 when

it is not missing. For online transactions, the median transaction value is $99 for both groups

(i.e., for transactions with and without a missing channel). It is worth noting that none of these

computations can directly test the exclusion restriction: whether the error term in an equation

explaining revenues is uncorrelated with the missing channel status. The calculations overall

provide a somewhat mixed picture regarding the relationship between the conditional moments

and the missing channel status, but it is not clear how to interpret this information in the current

context. For this reason, I also provide a more formal test of the identification strategy.

The test relies on the observation that, while the key variation suggested by the discussion

above is that of a “missing” vs. “nonmissing” channel, we can use the information regarding the

specific recorded channel (e.g., “Direct,” “Organic” etc.) to generate overidentifying restrictions.

By using dummy variables for those multiple specific channels as instruments for the single

endogenous variable (the DC dummy) we can apply the Sargan test to examine the extent to

which the moment conditions (i.e., the orthogonality of each of the instruments with the error

term) are consistent with the data. As the results section below demonstrates, the test is passed

in all but a single specification. Interestingly, this is the specification that uses all channel

dummies as instruments. Once the “Campaigns” instrument is omitted, the test is passed.

This is consistent with the concern raised above—given the high mean transaction value for

transactions in the “Campaigns” channel observed in Table 3 — regarding the validity of this

specific channel as an exclusion restriction.

To sum, as is usual, it is not possible to directly test an identifying assumption, and I have

reviewed above potential pitfalls that may violate it. Just the same, both qualitative and quan-

17

titative evidence provide support to the identifying assumption, and I rely on it in the regression

analysis provided below.

3 Estimation results

3.1 Sales representatives’ effect on revenue

The empirical analysis is conducted at the level of the individual customer. The dependent

variable is the total value ($US) of transactions signed with the client during the sample period.

We shall refer to this value as the total revenue extracted from the customer during the sample

period.15 Prior to computing this measure, I omit from the sample eight observations (five C1

transactions with values greater than 102$, and three A1 transactions with values in excess

of 1,100$) that represent clear data errors. In addition, I also remove transactions by the 35

customers who “switched” between online transactions, and transactions that involve a sales

representative due to the difficult-to-interpret status of DC for such customers, and the possibility

that these switches are actually capturing data errors (see the discussion in Section 2 above).

Following this removal of observations, I am left with 3,514 observations, relative to a total of

3,550 unique customers in the original dataset. This elimination of observations has no material

impact on the results.

The main explanatory variable is the dummy variable “DC,” taking the value 1 if the customer

engaged in communication with a sales team, and 0 otherwise (also referred to as the “DC dummy

variable”). The coefficient on this variable captures the effect of DC on the mean revenue

extracted per paying customer, i.e., the ARPPU. An additional explanatory variable is a dummy

variable taking the value 1 if the customer paid by wire transfer. Such transactions typically

involve relatively large amounts, and customers who performed them always communicate with a

sales representative. In order to avoid confounding this issue with the impact of DC interaction,

we control for this variable via an explanatory variable denoted wire max, taking the value 1

if the customer performed at least one transaction via wire transfer. The estimation equation

therefore takes the following simple form:

Total Revenue = α + β · wire max+ γ ·DC + ε

where ε is a disturbance term, and γ is the main coefficient of interest. Consistent with the

discussion of endogeneity in the sections above, the DC variable may be correlated with ε. I

therefore pursue both simple OLS specifications, and Two Stage Least Squares (TSLS) specifi-

15In transactions involving multiple months, it is possible that payment is received in installments over time, and someof these payments could, therefore, only be received by the Company after the sample period. For simplicity, we ignorethis issue and consider the total amount of transactions signed during the sample period as the total revenue obtainedfrom that customer over the eight studied months.

18

cations that utilize marketing channel dummy variables as instruments. Considering OLS first,

Table 7 shows that customers paying via wire transfer do tend to conduct larger transactions.

Whether or not we control for this, the effect of the DC variable on ARPPU is found to be

positive. Controlling for the wire transfer issue, interacting with a sales team increases ARPPU

by 117.2$.

Table 7: Regression Analysis: OLS

(1) (2)OLS1 OLS2

DC 392.4*** 117.2***(43.11) (42.01)

wire max 2,484***(109.0)

Constant 786.8*** 786.8***(22.71) (21.20)

Observations 3,514 3,514R-squared 0.023 0.149

Notes: Standard errors in parentheses, ***p < 0.01, ** p < 0.05, * p < 0.1

Results from the TSLS specifications are provided in Table 8. This table reports specifications

that vary the set of instruments used by choosing subsets of the six marketing channel dummy

variables (Campaigns, Paid, Direct, Affiliate, Organic, Referral).16

The bottom panel of Table 8 reports the first stage coefficients, i.e., the regression of the DC

dummy variable on the exogenous variables. As can be seen, in all specifications, the first-stage

coefficients are strongly significant, supporting the notion that marketing channels are positively

correlated with direct communication between the customer and a sales representative. The F-

statistics are quite large, noting that a statistic above the value of 10 is often viewed as reassuring

with respect to a “weak instruments” concern.

The top panel of Table 8 addresses the main research question in this paper. It shows, in

contrast to the OLS results in Table 7, that the effect of DC on ARPPU is negative and strongly

statistically significant. This finding is obtained regardless of which subset of the exclusion

restrictions are used. As discussed in detail in Subsection 2.2 above, several qualitative and

quantitative facts motivate the usage of dummy variables for marketing channels as exclusion

restrictions.16While all the TSLS specifications presented in Table 8 include the wire max variable, very similar results obtain when

it is omitted.

19

Tab

le8:

Tw

oS

tage

Lea

stS

qu

ares

anal

ysi

s

Second-sta

gere

sults

(1)

(2)

(3)

(4)

(5)

(6)

(7)

CP

DA

OR

PD

AO

RP

DO

RD

OR

DR

DO

OR

DC

-764

.2***

-841

.1**

*-9

27.9

***

-1,0

04**

*-7

56.2

***

-989

.8**

*-1

,669

***

(189.

7)(1

94.7

)(2

03.4

)(2

17.9

)(2

67.5

)(2

41.1

)(6

19.5

)w

ire

max

3,14

1**

*3,

199*

**3,

263*

**3,

320*

**3,

135*

**3,

309*

**3,

816*

**(1

79.

6)(1

83.2

)(1

89.2

)(1

98.7

)(2

28.0

)(2

12.9

)(4

79.4

)C

onst

ant

1,01

1**

*1,

031*

**1,

053*

**1,

072*

**1,

009*

**1,

069*

**1,

241*

**(5

2.0

5)(5

3.30

)(5

5.45

)(5

8.98

)(7

0.79

)(6

4.55

)(1

59.3

)

Sarg

anC

hi2

(df)

11.8

0(5

)6.

56(4

)3.

44(3

)2.

23(2

)0.

13(1

)2.

22(1

)0.

41(1

)S

arg

anp

-val

ue

0.0

380.

161

0.32

90.

328

0.72

00.

136

0.52

3O

bse

rvati

on

s3,5

14

3,51

43,

514

3,51

43,

514

3,51

43,

514

R-s

qu

are

d0.0

42

0.02

30.

044

First-sta

gere

sults

wir

em

ax

0.82

2**

*0.

821*

**0.

822*

**0.

817*

**0.

784*

**0.

803*

**0.

761*

**(0

.041

2)

(0.0

412)

(0.0

413)

(0.0

414)

(0.0

415)

(0.0

415)

(0.0

419)

Cam

paig

n0.

243**

(0.0

964)

Paid

0.2

23*

**

0.22

0***

0.21

3***

(0.0

474)

(0.0

474)

(0.0

474)

Dir

ect

0.21

7**

*0.

214*

**0.

206*

**0.

195*

**0.

151*

**0.

177*

**(0

.0169)

(0.0

168)

(0.0

167)

(0.0

166)

(0.0

158)

(0.0

163)

Affi

liat

e0.1

89***

0.18

6***

(0.0

533

)(0

.053

3)O

rgan

ic0.1

78***

0.17

5***

0.16

7***

0.15

6***

0.13

8***

0.07

80**

*(0

.0200

)(0

.020

0)(0

.019

9)(0

.019

8)(0

.019

6)(0

.019

0)R

efer

ral

0.2

07*

**

0.20

4***

0.19

7***

0.18

6***

0.14

2***

0.10

8***

(0.0

336

)(0

.033

6)(0

.033

6)(0

.033

6)(0

.033

4)(0

.033

6)C

on

stan

t0.1

35***

0.13

8***

0.14

5***

0.15

6***

0.20

1***

0.17

5***

0.23

5***

(0.0

111

)(0

.011

0)(0

.010

8)(0

.010

6)(0

.009

04)

(0.0

101)

(0.0

0836

)

R-s

qu

ared

0.13

30.

132

0.12

90.

124

0.10

80.

116

0.08

9F

-Sta

tist

ic76.

9488

.56

103.

5112

3.69

141.

6215

3.41

114.

24

Note

s:dep

enden

tva

riable

inth

ese

cond

stage

isto

talre

ven

ue

from

transa

ctio

ns

wit

hth

ecu

stom

er.

Dum

my

vari

able

sfo

rm

ark

etin

gch

annel

s(C

am

paig

ns,

Paid

,D

irec

t,A

ffiliate

,O

rganic

,R

efer

ral)

inst

rum

ent

for

the

DC

vari

able

.Sta

ndard

erro

rsin

pare

nth

eses

.***p<

0.0

1,

**p<

0.0

5,

*p<

0.1

20

In that discussion, based on a statistical analysis in Table 3, it was noted that three channels

— “Campaigns,” “Affiliate” and “Paid” — are characterized by higher mean transaction values

relative to other marketing channels. This is especially true for the “Campaigns” channel. The

top panel of Table 8 reports critical values and significance levels for the Sargan test of overiden-

tifying restrictions. The specification in column (1), that uses all six marketing channel dummy

variables as exclusion restrictions, fails this test with a p − value of 0.038. Column (2) shows

that once “Campaigns” is dropped from the instrument list, the p − value is 0.161. Additional

columns in the table reflect different specifications that drop the “Campaigns” channel, and, in

addition, drop one or both of the other channels that call for special attention, i.e., “Affiliate”

and “Paid.” Two patterns are clear: first, the negative effect of DC is strongly robust to such

variations over the set of instruments used. Second, all these specifications pass the Sargan test.

Of course, passing this test does not prove that the specification is correct. It does, however,

imply that the set of moment conditions generated by the exclusion restrictions is not rejected

by the data.

To summarize, the regression analysis delivers the robust result that interaction with the

Company’s sales team decreases the mean per paying customer revenue. As discussed above,

this result does not indicate that sales representatives do not perform well. Rather, it highlights

a nuanced view of their contribution: sales representatives may be especially effective in drawing

customers with a low inherent willingness-to-pay for the product into the customer pool, who,

absent this interaction, would likely refrain from making any purchase from the Company. These

customers, given the interaction, tend to perform small transactions, and this offsets a likely

positive effect that sales representatives have on the extracted value from customers with higher

spending potential.17

3.2 Sales representatives’ effect on repeat purchase

The results above motivate some additional exploration of the sales team’s impact. Specifically,

conditional on an initial transaction taking place, does interaction with the sales team increase

the probability of repeat purchase?

The raw data indicate a plausible negative relationship between the extent of repeated trans-

actions and DC. In particular, the fraction of transactions involving DC is decreasing with the

total number of transactions, as conveyed by Table 9 below.

This descriptive evidence, however, is only suggestive of a negative relationship. First, it does

not address endogeneity concerns. Second, it is derived from a sample which may be somewhat

inefficient for the relevant purposes: for instance, a customer whose first transaction involved,

17A referee suggested that using the log of revenues, rather than their level, as the dependent variable will reduce theimpact of large transactions. Table 11 in the appendix presents such results, in a manner parallel to Tables 7 and 8 above.As can be seen there, this leads to results that are qualitatively the same: the effect of DC is found to be positive underOLS, and negative under TSLS.

21

Table 9: Repeated Transactions and DC

Number of transactions DC=0 DC=1 DC=1 fraction

1 729 471 0.392 376 181 0.323 257 104 0.294 184 58 0.245 163 54 0.256 150 29 0.167 486 54 0.108 189 22 0.109 4 2 0.3310 1 0 0.00

say, a 12-month subscription cannot possibly make a repeat purchase during our observed sample

period of eight months. Some restriction on the sample is, therefore, desirable.

I therefore proceed with formal regression analysis in which I restrict attention to 2,840 cus-

tomers (of a total of 3,550, or 3,514 following omissions described above) whose first transaction

involved a one-month subscription, i.e., it involved purchase of A1, B1 or a C1 package. Focusing

attention on these customers offers a practical, albeit imperfect, way of avoiding biases stemming

from sample length. A limitation of this approach is that we cannot tell with certainty whether

the customer’s observed first transaction is really the first transaction—this would not be the

case had the customer performed a previous purchase prior to the observed sample period. De-

spite this limitation, restricting the sample in this way allows us to focus on the key question of

interest.

The results of this analysis are provided in Table 10. Columns (1)-(3) report regressions that

use the total number of transactions with the customer as the dependent variable. This analysis

is performed using OLS, Poisson regression and a TSLS regression with the same instrumental

variables as in the ARPPU analysis (i.e., marketing channel dummy variables). The use of the

Poisson regression is appropriate since the dependent variable is a count variable, while the TSLS

approach (which again uses the marketing channel dummy variables as instruments for the DC

dummy variable) allows us to correct the results for the same endogeneity issues discussed in the

analysis of revenues offered above.

Using all three different specifications, the effect of sales team interaction is found to be

negative and statistically significant. That is, conditional on an initial (one-month) transaction

taking place, the total number of transactions with a customer is lower when the customer

interacts with a sales representative. This result holds with and without accounting for the

endogeneity of that interaction. This result is consistent with the earlier ARPPU results from

Section 3.1 above, and suggests yet another aspect of the interaction with a sales representative.

22

The likely interpretation is, again, that sales representatives are mostly effective in drawing low-

willingness-to-pay customers into the customer pool, and these customers are less likely to engage

in repeat purchases over time relative to higher willingness-to-pay customers.

Table 10: Repeat purchase analysis

(1) (2) (3) (4) (5)

# transactions # transactions # transactions Multiple transactions Multiple transactionsOLS Poisson IV OLS IV

DC -0.880*** -0.242*** -8.954*** -0.0739*** -0.808***(0.113) (0.0252) (0.996) (0.0196) (0.126)

Constant 4.098*** 1.411*** 5.799*** 0.778*** 0.933***(0.0519) (0.0105) (0.224) (0.00901) (0.0283)

Observations 2,811 2,811 2,811 2,811 2,811R-squared 0.021 0.005

Notes: Dependent variable in columns (1)-(3) is the total number of transactions with the client during the sample period. In columns(4)-(5), it is a dummy variable taking the value 1 if the customer performed more than one transaction, and zero otherwise. Standarderrors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

The results in columns (1)-(3) are not entirely easy to interpret, however, for the following

reason: imagine that, having performed an initial purchase of a one-month subscription, a cus-

tomer is convinced by a sales representative to purchase an expensive 12-month subscription.

Since the dependent variable merely counts transactions, it will take the value of 2, whereas

for another customer, who made five consecutive purchases of one-month subscriptions, the de-

pendent variable would take the value of 5, despite the fact that this is, in some sense, a less

successful outcome for the Company. Columns (4)-(5) of the table attempt to overcome this

issue by considering a binary dependent variable, taking the value 1 if the customer performed

more than one transaction, and zero otherwise. This approach allows for a clean analysis of a

simple question: what is the effect of interacting with a sales representative on the probability

of there being any repeat purchase by the customer? As the results in these columns indicate,

this effect is, once again, negative, reaffirming the message delivered by the results in columns

(1)-(3) and consistent with the overall analysis.

4 Concluding Remarks

This paper adds to a literature that studies the impact of sales representative on the revenue

extracted from customers. To this end, I use a dataset that records the incidence of such commu-

nication, allowing me to investigate its impact on the history of customer transactions relative to

an alternative channel involving online transactions. The existence of two parallel channels, and

23

the availability of transaction level data indicating whether communication with a representative

took place, provides a unique research design within which the impact of sales representatives can

be investigated. Another important aspect of the data is the availability of a natural exclusion

restriction, allowing me to address the difficult endogeneity problem embedded in the statistical

exercise.

The picture that emerges from the analysis is quite consistent across different empirical spec-

ifications: communication with a sales representative is associated with lower average revenue,

and with lower client retention. My interpretation of this finding is that it does not reflect poor

performance by the sales team. Quite to the contrary, it suggests that a major aspect of the sales

team is its ability to attract customers with a low a-priori willingness to pay for the product.

This extensive margin effect appears to dominate an intensive margin effect (i.e., increasing the

willingness to pay of customers conditional on purchase) on both average revenues and repeat

purchase behavior.

Given the large investments in Customer Relationship Management across firms and sectors in

the modern economy, it is important to provide evidence on the effectiveness and consequences of

sales teams. Surveys suggest that many managers are skeptical about the returns on such invest-

ments. This paper makes a modest contribution to a body of empirical evidence on the nature

of salespersons’ contribution, with the hope of improving our understanding of this important

topic.

A clear limitation of this work is its reliance on data from a single firm. Nonetheless, it is

this specificity that allows me to take an in-depth view of transaction level data, and to develop

an identification strategy that overcomes an important endogeneity problem. Future research

should hopefully continue to explore this issue using additional data sources.

References

[1] Albers, S., Murali K. M., and S. Sridhar, (2010), “Personal Selling Elasticities: A Meta-

Analysis,” Journal of Marketing Research, 47(5), 840-53

[2] Angrist, J., and J.S. Pischke, (2008), “Mostly Harmless Econometrics,” Princeton Univer-

sity Press, Princeton, New Jersey.

[3] Bagwell, K, (2003), “The Economic Analysis of Advertising,” In Mark Armstrong and Rob

Porter (eds.), Handbook of Industrial Organization, North-Holland: Amsterdam

[4] Berndt E. R. (1991), “The Practice of Econometrics: Classic and Contemporary,” Reading,

MA: Addison-Wesley

[5] Berndt E. R., Pindyck, R., and P. Azoulay (2000), “Consumption Externalities and Diffu-

sion in Pharmaceutical Markets: Anti-Ulcer Drugs,” NBER Working paper 7772, National

Bureau of Economic Research

24

[6] Berry, T.S., Carnall, M., and P.T. Spiller (1996), “Airline Hubs: Costs, Markups and the

Implications of Customer Heterogeneity,” NBER Working Paper 5561, National Bureau of

Economic Research

[7] Borzekowski, R., R. Thomadsen, and C. Taragin (2009), “Competition and Price Discrim-

ination in the Market for Mailing Lists,” Quantitative Marketing and Economics, 7, 147-79

[8] Chintagunta, P.K. (2001), “Endogeneity and Heterogeneity in a Probit Demand Model:

Estimation Using Aggregate Data,” Journal of Marketing Research, 20(3): 442-56

[9] Churchill, G.A., Ford, N.M., Hartley, S.W. and Walker, O.C. (1985): “The determinants

of salesperson performance: a meta-analysis,” Journal of Marketing Research, Vol. XXII,

May, pp. 103-18.

[10] Clerides, S. (2002), “Book value: intertemporal pricing and quality discrimination in the

US market for books,” Journal of Industrial Organization , 20, 1385-408

[11] Clerides, S. (2004), “Price discrimination with differentiated products: definition and iden-

tification,” Economic Inquiry, 42, 402-12

[12] Cohen, A. (2008), “Package size and price discrimination in the paper towel market,”

International Journal of Industrial Organization, 26, 502-16

[13] Elberse, A., and B. Anand (2005): “Advertising and Expectations: The Effectiveness of

PreRelease Advertising for Motion Pictures,” Harvard Business School Working Paper

Series,, No. 05-060

[14] Horsky, D., and P. Nelson (1996): “Evaluation of Salesforce Size and Productivity Through

Efficient Frontier Benchmarking,” Marketing Science,, 15(4): 301-20

[15] Jackson, D.W. Jr., Schlacter J.L., and W.G. Wolfe (1995): “Examining the Bases Utilized

for Evaluating Salespeoples’ Performance,” Journal of Personal Selling and Sales Manage-

ment, 15(4) pp. 57-65

[16] John, G., and B. Weitz (1989): “Salesforce Compensation: An Empirical Investigation of

Factors Related to Use of Salary Versus Incentive Compensation,” Journal of Marketing

Research, 26: 1-14

[17] Jayachandran, S., Sharma, S., Kaufman, P., and P. Raman (2005): “The Role of Relational

Information Processes and Technology Use in Customer Relationship Management,” Jour-

nal of Marketing, 69(4): 177-192

[18] Kuster, I. and P. Canales, (2008): “Some determinants of salesforce effectiveness,” Team

Performance Management: An International Journal, 74(7/8). 296-326

[19] Leslie, P. (2004), “Price Discrimination in Broadway Theatre,” Rand Journal of Economics,

35 (3), 520-41

[20] Narayanan, S., R. Desiraju, and P.K. Chintagunta (2004), “Return on Investment Impli-

cations for Pharmaceutical Promotional Expenditures: the Role of Marketing-Mix Inter-

actions,” Journal of Marketing, 68: 90-105

25

[21] Peppers, D., Rogers, M., and b. Dorf (1999), “Is Your Company Ready for One-to-One

Marketing,” Harvard Business Review, 77 (1), 151-161

[22] Rich, G.A., Bommer, W.H., MacKenzie, S.B., Podsakoff, P.M. and J.L. Johnson (1999):

“Apples and Apples or Apples and Oranges? A Meta-Analysis of Objective and Subjective

Measures of Salesperson Performance,” 19(4) pp. 41-52

[23] Rigby, D.K., Reichheld, F., and P. Schefter (2002), “Avoid the Four Perils of CRM,”

Harvard Business Review, 80 (2), 101-109

[24] Schmalensee R. (1972), “The Economics of Advertising,” Amsterdam: North-Holland

[25] Simester, D. and J. Zhang (2014), “Why Do Salespeople Spend So Much Time Lobbying

for Low Prices?,” Marketing Science 33(6): 796-808

[26] Verboven, F. (1996), “International price discrimination in the European car market,”

Rand Journal of Economics, 27, 240-68

26

A Results using logs

Table 11: Regression analysis using log revenues as the dependent variable

OLS

(1) (2)

DC 0.313*** 0.154***(0.0386) (0.0392)

wire max 1.433***(0.102)

Constant 6.187*** 6.187***(0.0203) (0.0198)

Observations 3,514 3,514R-squared 0.018 0.071

TSLS(1) (2) (3) (4) (5) (6) (7)

C P D A O R P D A O R P D O R D O R D R D O O R

DC -0.984*** -0.965*** -1.044*** -1.122*** -0.739*** -1.058*** -2.286***(0.186) (0.188) (0.197) (0.211) (0.252) (0.232) (0.681)

wire max 2.281*** 2.268*** 2.326*** 2.384*** 2.099*** 2.337*** 3.252***(0.176) (0.177) (0.183) (0.193) (0.215) (0.205) (0.527)

Constant 6.476*** 6.471*** 6.491*** 6.511*** 6.414*** 6.495*** 6.808***(0.0510) (0.0515) (0.0537) (0.0572) (0.0668) (0.0621) (0.175)

Observations 3,514 3,514 3,514 3,514 3,514 3,514 3,514

Notes: dependent variable in the second stage is the log of total revenue from transactions with the customer. OLS spec-ifications correspond to those of Table 7, and TSLS ones correspond to Table 8. Dummy variables for marketing channels(Campaigns, Paid, Direct, Affiliate, Organic, Referral) instrument for the DC variable. Standard errors in parentheses. ***p < 0.01, ** p < 0.05, * p < 0.1

27