Estimating the Impact of Sales Representatives On the Average

Estimating the Impact of Sales Representatives On the Average
Revenue Per Customer∗
Alon Eizenberg
Department of Economics, the Hebrew University of Jerusalem, and CEPR
April 2015
Abstract
Companies make extensive use of sales representatives with the aim of improving their ability
to capture value from customers. Little empirical evidence, however, is available with regard to
the impact of sales representatives on customer willingness-to-pay (WTP) and the firm’s revenues.
This paper uses a unique transaction-level dataset from a company in the High Technology sector
to shed light on this issue by studying the impact of interaction with sales representatives on the
Average Revenue Per User (ARPU). A major challenge is that the interaction may be correlated
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. Descriptive
analysis indicates that interaction with a representative results in a higher incidence of longer subscription periods, consistent with institutional details confirmed by the company. Furthermore, naive
regression results imply that, conditional on purchase, interaction with representatives substantially
increases the 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 additional finding is that interaction with a sales representative has a negative effect on the probability of repeat purchase, conditional on an initial transaction taking place. These results indicate
that sales representatives draw 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 their
data available, and for their helpful feedback. I thank Saul Lach for helpful comments. Orry Kaz provided excellent
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 scarce and mixed, and this paper wishes to
make a modest step in the direction of filling that gap.
Similarly as with advertising, direct communication (hereafter DC) between sales representatives 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 Relationship 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 heterogeneous 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 success
are mixed: according to a study by the Gartner Group, 55% of all CRM projects fail to produce
results (Rigby et al. 2002). An empirical analysis of the effectiveness of direct communication
with customers may help us obtain a better understanding of the impact of such investments.
This paper measures the effectiveness of direct communication with customers by utilizing
2
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 choose to 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. This approach effectively focuses the analysis on the impact of DC on the Average
Revenue Per User (ARPU). The ARPU statistic is widely used by practitioners (e.g., analysts)
to evaluate company performance. It is, therefore, of interest to investigate the manner with
which it is affected by interacting with a sales representative.
Descriptive analysis reveals that DC is associated with a higher incidence of long subscription
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 analysis
demonstrates that DC has a positive, statistically as well as economically significant effect 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: potential customers had the freedom to
choose whether or not to engage in DC. If customers who were a-priori willing to pay higher
prices for the Company’s product also displayed an above-average tendency to engage in 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.
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 the current context,
1
“Enterprise” customers are handled via a specialized sales team, and tend to purchase rather expensive (and sometimes
tailor-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 via
this channel.
3
the interaction with a sales representative is endogenous since it is correlated with unobserved
characteristics at the level of the individual customer. This 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
quite 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
the dataset available in this paper, an instrumental variable strategy is suggested to overcome
the endogeneity problem.
The identifying strategy is based on the fact that transactions are characterized by a “marketing channel” that is observed in the data. The marketing channel captures the manner by which
the customer arrived at the website (for example, whether via a search engine, by clicking on
a paid link, or via an affiliate website). The identifying assumption is that this technical information is not correlated with the customer’s unobserved willingness-to-pay. Probit regressions
indicate that this variable does have a significant effect on the probability of engaging in interaction with a sales representative, a finding which is consistent with institutional details: in some
cases, the interaction was initiated by a sales representative in response to the customer’s visit at
the website (e.g., a download of a free version of the product) that caused the customers’ details
to be recorded. This creates 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 negative 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. Interaction with a sales
representative, therefore, decreases the average revenue per-customer, conditional on a purchase
taking place. The unconditional impact of DC on extracted revenue, however, is not identified,
since the data only informs us about transactions that actually took place.
I interpret this finding as follows: sales representatives have two systematic effects on customer
purchase decisions. First, conditional on purchase, 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 indicate that the second effect is strong enough to offset
4
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.
These findings indicate a nuanced effect of sales representatives on firm performance: while
they can expand the company’s customer base, this expansion results in a lower ARPU measure.
Since both statistics (customer base and ARPU) 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 addresses the evaluation of salesforce effectiveness
and of its contribution to the organization’s success. 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 sales force can be a useful “shortcut” to understand many
aspects of corporate success. One of the main practical tasks for this literature is to identify
optimal sales force compensation schemes. 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 K¨
uster 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
5
quota.2 The current paper contributes to this vast literature by offering a unique research design:
first, I focus on transactions at the level of the individual customer, rather than at the level of
an individual sales person. Second, I compare two parallel channels through which customers
may make purchases, one being an “anonymous” online channel, and the second involving direct
communication with a sales person. Finally, I offer an identification strategy that overcomes the
endogeneity of communicating with a sales person. To the best of my knowledge, these features
of the research design differentiate this paper from previous work in this area.
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
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.3
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.4
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 that an average 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
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
2
This observation derives from meta-analysis in Churchill et al. (1985).
A caveat is that, if a customer conversed with a sales team, but did not complete a transaction, and later performed
the transaction online creating a new ID, the analysis is likely to miss the fact that DC occurred. Unfortunately, the data
do not allow me to address this issue.
4
The data also contain very few transactions that do not fall into this classification. Six transactions are missing the
plan description information, and an additional 268 transactions belong in two other small plans.
3
6
valid beyond it). This 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 ARPU 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
A
B
C
1
3
6
12
990
290
99
2,000
600
240
4,950
1,450
495
7,900
2,600
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 transactions. 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)*99.5 Some 184 additional “non-modal” cases are clear promotional discounts that are
recurring (e.g., a 20$ discount over the modal price).
5
Since the transaction time within the day is not recorded, I considered the bounds of (3/31)*99 and (5/31)*99 in such
cases. The 92 cases alluded to above are those in which the discount fell in between the bounds.
7
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.
Insights into the research question can be obtained by examining the distribution of transactions 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 communication (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 Company, 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 contact a
representative. The formal regression analysis provided in the section below addresses 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 subscriptions,
they may be achieving this goal via promotional discounts, thus offsetting some of the positive
8
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
A
B
C
1
3
6
12
Total
% one-month
26
854
1,024
13
172
68
16
53
27
21
104
33
76
1,183
1,152
34.2
72.2
88.9
Transactions with DC=0
Quality/duration
A
B
C
1
3
6
12
Total
% one-month
89
3,067
6,037
0
25
33
8
77
80
5
53
59
102
3,222
6,209
87.3
95.2
97.2
Negligible plans omitted.
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 contact. 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 engine) and “Campaigns.” Of note, this
information is not available for about half of the transactions. 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 marketing” variable which
is typically, but not always, identical to the “first marketing” variable that is used in the analysis
and discussed above.
The table indicates that about half of the 12,218 transactions have a missing value for the
marketing channel variable. Among those transactions with a non-missing channel, “Direct”
and “Organic” are the most heavily represented channels. The average transaction values in
these channels, as well as in “Referral” and in the “Missing” category, appear similar, suggesting
no systematic effect of the marketing channel on transaction values. Several smaller channels,
specifically, “Campaigns,” “Affiliate” and “Paid,” reflect higher mean transaction values.
9
Table 3: Marketing Channels
Marketing Channel
Affiliate
Campaigns
Direct
Organic
Paid
Referral
Missing
# transactions
Mean transaction value ($US)
211
45
3213
1852
247
518
6132
361.66
608.90
256.70
250.64
305.47
258.11
262.75
The identifying assumption, according to which the marketing channel can be excluded from
an estimation equation that explains revenues, is therefore largely supported by the raw data.
Just the same, I report below robustness checks in which dummy variables for the three channels
“Campaigns,” “Affiliate” and “Paid” are not used as instrumental variables, to guard against
the possibility that they are correlated with underlying consumer valuations. As shown below,
the results are qualitatively robust to this issue.
To serve as effective instruments for direct communication with a sales representative, the
marketing channel should also be correlated with the endogenous variable, the DC dummy variable. Simple descriptive evidence is consistent with this hypothesis: 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 4 below:
Table 4: Marketing channel and DC Communication by Plan
Transaction type
C1
C3
C6
C12
B1
B3
B6
B12
A1
A3
A6
A12
% DC With Channel
% DC Without Channel
21.4%
74.6%
43.9%
63.0%
26.9%
89.3%
46.6%
70.4%
46.2%
100.0%
70.0%
85.7%
8.6%
50.0%
13.6%
24.6%
14.6%
78.9%
28.6%
62.8%
10.5%
0.0%
64.3%
78.9%
Negligible plans (described above) omitted.
The notion that having a recorded marketing channel increases the probability of DC is consistent with institutional details reviewed above. Specifically, a recorded channel is associated with
10
a greater ability and motivation of the sales team to track the customer and pursue interaction
(say, following a free download from the company’s website). These results support the use of
these channels as effective instruments for DC interaction.
To provide an initial, descriptive outlook on this relationship, I perform a Probit analysis which
results are provided in Table 5. The dependent variable is the DC dummy variable (standard
errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1). 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 interaction. The constant term captures the omitted effect of a missing
marketing channel, which is negative. In the formal regressions analysis offered below, the unit
of observation would be the individual customer rather than the individual transaction, and
the revenue garnered from the customer would be regressed on the DC variable via Two-Stage
Least-Squares, using the marketing channels as instruments.6
Table 5: Probit Analysis
Campaign
0.967***
(0.189)
0.579***
(0.0874)
0.587***
(0.0313)
0.793***
(0.0908)
0.524***
(0.0376)
0.504***
(0.0631)
-1.164***
(0.0206)
Paid
Direct
Affiliate
Organic
Referral
Constant
Observations
12,218
See text.
6
As reported there, a TSLS analysis is used with a linear first-stage, as opposed to the nonlinear first stage reflected
in the Probit analysis, presented here for descriptive purposes only. This avoids the “forbidden regression” issue raised by
Angrist and Pischke (2008).
11
3
Estimation results
3.1
Sales representatives’ effect on ARPU
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.7 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 customer, i.e., the ARPU. An additional explanatory variable of interest
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 speak to 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. Consistent with the discussion
of endogeneity in the sections above, each regression is performed twice: via simple OLS, and
using linear Two-Stage Least-Squares (TSLS) with the dummy variables for marketing channels
serving as instruments for the DC variable.
The results of this analysis are offered in Table 6. As columns (3) and (4) show, customers
paying via wire transfer tend to conduct larger transactions. Irrespective of whether we control
for this issue or not, the effect of the DC variable on ARPU is found to be positive in a simple
OLS regression, but negative in the IV analysis (as shown by comparing column (1) to column
(2), and column (3) to column (4)). Focusing on the third and fourth columns, the OLS estimate
suggests that interacting with a sales team increases ARPU by 117.2$, but the IV results show
quite the opposite, reporting that such interaction reduces ARPU by a sizable 764.2$.
7
In transactions involving multiple months, it is possible that payment is received in installments over time, and some
of these payments could, therefore, only be received by the Company after the sample period. For simplicity, we ignore
this issue and consider the total amount of transactions signed during the sample period as the total revenue obtained
from that customer over the eight studied months.
12
Table 6: Regression analysis
DC
(1)
(2)
(3)
(4)
OLS1
TSLS1
OLS2
TSLS2
392.4***
(43.11)
-1,508***
(288.0)
786.8***
(22.71)
1,314***
(83.46)
117.2***
(42.01)
2,484***
(109.0)
786.8***
(21.20)
-764.2***
(189.7)
3,141***
(179.6)
1,011***
(52.05)
3,514
0.023
3,514
3,514
0.149
3,514
0.042
wire max
Constant
Observations
R-squared
Dependent variable is total revenue from transactions
with the customer. Instrumental variables in the TSLS
regressions are dummy variables for marketing channels.
Standard errors in parentheses. *** p < 0.01, ** p < 0.05,
* p < 0.1.
We next consider the robustness of the qualitative pattern found in Table 6 to the choice
of instrumental variables. Specifically, we wish to consider combinations of marketing channel
dummy variables that exclude the channels which exclusion from the main regression could
raise some concern following the discussion in Section 2 (namely, “Campaigns,” “Paid” and
“Affiliate”). The results of this analysis are presented in Table 7. The negative impact of sales
team interaction on ARPU is maintained, implying that the results are robust to issues associated
with the choice of instrumental variables.8
To summarize, the regression analysis delivers the robust result that interaction with the
Company’s sales team decreases the mean per-customer revenue. As discussed above, since this
analysis is conditioned on the customer making purchases (otherwise we would not observe the
customer), 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. These customers 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.
8
Consistent with Section 2, another robustness check performed involved estimating the model without the “non-modal”
transactions, delivering similar results. Additional details on these sensitivity checks are available from the author upon
request.
13
Table 7: Robustness to choice of IVs
Direct Organic Referral Direct Referral Direct Organic
DC
wire max
Constant
Observations
R-squared
Organic Referral
-1,004***
(217.9)
3,320***
(198.7)
1,072***
(58.98)
-756.2***
(267.5)
3,135***
(228.0)
1,009***
(70.79)
-989.8***
(241.1)
3,309***
(212.9)
1,069***
(64.55)
-1,669***
(619.5)
3,816***
(479.4)
1,241***
(159.3)
3,514
3,514
0.044
3,514
3,514
Standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.
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 transactions and DC communication. In particular, the fraction of transactions involving DC is
decreasing with the total number of transactions, as conveyed by Table 8 below:
Table 8: Repeated Transactions and DC
Number of transactions
DC=0
DC=1
DC=1 fraction
1
2
3
4
5
6
7
8
9
10
729
376
257
184
163
150
486
189
4
1
471
181
104
58
54
29
54
22
2
0
0.39
0.32
0.29
0.24
0.25
0.16
0.10
0.10
0.33
0.00
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,
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.
14
I therefore proceed with formal regression analysis in which I restrict attention to 2,840 customers (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.
The approach of focusing attention on customers whose first transaction involves a one-month
subscription 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. Despite 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 9. 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 ARPU 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 allows us to correct the results for endogeneity issues involving the explanatory
variable, the DC dummy variable.
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 ARPU results from
Section 3.1 above, and suggests yet another aspect of the interaction with a sales representative.
The likely interpretation is, again, that sales representatives are mostly effective in drawing lowwillingness-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.
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 customer
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 dependent
variable would take the value of 5, despite the fact that this is, in some sense, a less desirable
outcome for the firm. Columns (4)-(5) of the table attempt to overcome this issue by considering
a binary dependent variable, taking the value 1 if the costumer performed more than one transaction, and zero otherwise. This approach allows 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
15
Table 9: Repeat purchase analysis
DC
Constant
(1)
(2)
(3)
(4)
(5)
# transactions
OLS
-0.880***
(0.113)
4.098***
(0.0519)
# transactions
Poisson
-0.242***
(0.0252)
1.411***
(0.0105)
# transactions
IV
-8.954***
(0.996)
5.799***
(0.224)
Multiple transactions
OLS
-0.0739***
(0.0196)
0.778***
(0.00901)
Multiple transactions
IV
-0.808***
(0.126)
0.933***
(0.0283)
2,811
0.021
2,811
2,811
2,811
0.005
2,811
Observations
R-squared
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.
Standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.
our overall analysis.
4
Concluding Remarks
This paper studies an issue on which little empirical evidence exists to date: the impact of
communicating with a sales representative on the revenue extracted from customers. The paper
utilizes a unique dataset that records the incidence of such communication, 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 the availability of
transaction level data indicating whether communication with a representative occurred or not,
therefore provides a unique opportunity to shed light on this issue. Another unique 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 specifications: 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 throughout firms and
sectors in the modern economy, it is important to provide evidence on the effectiveness and
16
consequences of sales teams. The extant literature offers mixed evidence on this issue, often
based on surveys of managers. This suggests that many managers are skeptical about the returns
on such investments. By providing statistical evidence on the actual impact of interaction with
sales representatives, I hope to make a modest contribution to a better 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.
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