Marketing Science Institute Working Paper Series 2012
Report No. 12-112
Do Marketing Campaigns Produce Multichannel Buying
and More Profitable Customers? A Field Experiment
Elisa Montaguti, and Scott A. Neslin, and Sara Valentini
“Do Marketing Campaigns Produce Multichannel Buying and More Profitable Customers? A
Field Experiment,” Elisa Montaguti, and Scott A. Neslin, and Sara Valentini © 2012; Report
Summary © 2012 Marketing Science Institute
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Report Summary
One of the most intriguing and managerially relevant findings in the multichannel customer
management literature is the positive association between the multichannel customer and profits.
The question is whether this is an actionable, causal relationship. Specifically, can marketing
campaigns be designed to turn single-channel customers into multichannel customers, and in turn
will these multichannel customers become more profitable to the firm?
Elisa Montaguti, Scott Neslin, and Sara Valentini conduct a field experiment to investigate this
question. In their study, they manipulate two key aspects of campaign design: message and
incentives. They investigate two messagesone which directly communicates the benefits of
multichannel shopping, the other which emphasizes the core value proposition of the firm. They
also investigate the provision of financial incentives in the form of coupons and compare it to the
case where no financial incentives are provided.
Findings
The multichannel message coupled with no financial incentives produced more multichannel
customers and increased revenues more than other campaign designs. The authors estimate the
profit ROI of this strategy to be 40%. These results suggest that the multichannel message
motivated customers to explore the benefits of multichannel buying, and as they did so, they
became more satisfied customers and hence purchased more over time.
Further, this multichannel message/non-financial incentive campaign worked best on customers
who otherwise would be predicted not to become multichannel on their own. Using an a priori
estimated model, the authors estimate that targeting just customers with low pre-disposition
towards multichannel shopping would generate a 51% ROI.
The multichannel message/financial incentive campaign did not significantly increase either
multichannel channel behavior or revenues. It had a one-year ROI of -142%. The results suggest
that the poor performance of the multichannel/financial campaign resulted from its inability to
motivate customers to explore different channels. The campaign forced customers to shop in
channels they might not like in order to take advantage of the financial offer, negatively affecting
satisfaction with the shopping experience.
The value proposition campaigneither with or without incentivesdid not increase either
multichannel behavior or revenues. The ROIs for these campaigns were -104% and -106%
respectively.
Managerial implications
A multichannel/nonfinancial campaign raising customer awareness of the availability of a firm’s
different points of contact is inexpensive and effective at generating multichannel shoppers and
increasing profits. This strategy acts to modify customers’ channel shopping behavior at a lower
cost than financial multichannel incentives. The multichannel shoppers who respond to this
campaign generate more profit and are therefore more valuable to the firm.
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Further, financial incentives might be a loss-generating activity for firms because they are not
only costly but do not increase multichannel shopping.
Conclusion
This study is contingent on the particular firm that participated, as well as the specific messages
and incentives. However, the research clearly builds the knowledge base in this critical area by
demonstrating the effectiveness of multichannel campaigns, offering guidance on the design of
those campaigns, and demonstrating the potential for targeting based on an a priori estimated
model of the customer’s multichannel potential. Importantly, it demonstrates that research on the
multichannel/profitability relationship can be translated into practice, and verifies the causal link
from marketing actions to multichannel customer behavior to customer profitability.
Elisa Montaguti is Associate Professor of Marketing, Department of Management, University of
Bologna, Italy. Scott A. Neslin is Albert Wesley Frey Professor of Marketing, Tuck School of
Business, Dartmouth College. Sara Valentini is Assistant Professor of Marketing, Department of
Management, and University of Bologna, Italy.
Acknowledgements
The authors thank an anonymous retailer for cooperating in the implementation of this field
experiment. This research is supported by the Marketing Science Institute and the Wharton
Interactive Media Initiative as a winner of the “Modeling Multichannel Customer Behavior”
research proposal competition.
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Introduction
The ever-expanding multiplicity of channels through which customers can purchase from
companies has produced the “multichannel customer”. An intriguing finding related to
multichannel customers is the growing consensus among academics and practitioners that
multichannel customers buy more and are more valuable than single channel customers. This
finding is highly important because it suggests a customer management strategy for increasing
customer value – undertake marketing campaigns that produce more multichannel customers.
Such customers should produce higher revenues and profits, thereby increasing their value to the
firm.
However, recent literature points out that we still do not know the extent to which this
positive association between multichannel behavior and customer value is actionable (Neslin and
Shankar 2009). One issue of course is whether the association is causal or due to spurious
factors such as self-selection or high levels of marketing directed at multichannel customers
(Blattberg, Kim, and Neslin 2008). But even if the result is causal in the statistical sense, the
studies that find this positive relationship are conducted in a status quo environment. That
environment produces a certain amount of multichannel behavior and a certain amount of profits.
The question is whether the firm can proactively intercede in that environment and create more
multichannel behavior and more customer value.
Hence, the purpose of our work is to answer four main questions: (1) Can newly-acquired
customers be turned into multichannel customers? (2) Does the newly created multichannel
customer become more valuable to the firm? (3) How in practical terms can this transformation
be accomplished, in particular, what types of messages and incentives work best? (4) Can the
customers most likely to react favourably be identified and hence targeted in future campaigns?
An important theme to the above questions is, can we establish a causal link from marketing
actions to customer multichannel behavior to customer profitability. Our approach to
establishing this causality is to investigate these questions through a field experiment. The field
experiment allows us to establish causality both in the creation of multichannel customers and in
the generation of higher profits (Cook and Campbell 1979). And most importantly, it shows
whether an active intervention by the firm can produce high value multichannel customers.
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The field experiment is conducted with the cooperation of a multichannel book retailer. Two
cohorts of newly acquired customers are available. The first contains customers acquired at the
end of 2009 and monitored over 2010 and is used to develop a model that predicts the potential
for a customer to become multichannel. This model is used as a covariate to analyze the results
of the field experiment, and to investigate whether particular customers can be targeted based on
their multichannel potential. The second cohort includes customers acquired at the end of 2010.
The field experiment is conducted on the second cohort whereby customers are randomly
assigned to experimental treatments and multichannel shopping buying behavior is monitored for
a full year (January 2011-January 2012).
The field test entails two factors – “message” and “incentive”. Both factors contain two
levels. The message factor is either an explicit invitation for the customer to become
multichannel or a more general message communicating the value proposition of the firm. The
incentive factor entails either the provision of price discount coupons or no coupons provided.
We find that:
1. The multichannel message coupled with no financial incentive achieves the desired
results. It produces more multichannel customers and increases revenues. We estimate
the profit ROI of this strategy to be 40%.
2. The multichannel message/no-financial incentive campaign works best on customers who
otherwise would be predicted not to become multichannel on their own. These customers
can be identified by an a priori estimated model. Profitability of a well-targeted
campaign would return more than the 40% ROI achieved by targeting all customers,
specifically 51% ROI by targeting just the customers with low pre-disposition towards
multichannel shopping.
3. The multichannel message/financial-incentive campaign does not significantly increase
either multichannel channel behavior or revenues. As a result it produces a loss, a oneyear ROI of -142%.
4. Both the value proposition campaigns – incentive and no incentive –do not increase
either multichannel behavior or revenues. The ROIs for these campaigns are -104% and 106% respectively.
The most important result is that indeed, management can proactively create profitable
multichannel customers. However, our findings suggest that identifying the right combination of
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message and incentive is a challenge. Our particular results highlight the importance of message
over financial incentive as a strategy for creating profitable multichannel customers. However,
for a different company with a different customer base, the optimal campaign could vary. What
we learn crucially is that (1) the multichannel/profitability strategy can work, and (2) companies
will want to pre-test the particulars of the message/incentive aspects of their campaign. This pretesting is especially valuable because as we show in conclusion (2) above, the results can be used
to develop a targeting strategy for reaching the customers most likely to become multichannel.
Theory and Evidence Regarding the Multichannel-Profitability Link
Why multichannel customers may become more profitable
There are several reasons why the multichannel customer might be more profitable (see
Blattberg, Kim and Neslin 2008; and Neslin and Shankar 2009 for a review): (1) self-selection,
(2) marketing, and (3) customer loyalty. The self-selection explanation is that high volume
customers have more purchase occasions; hence they naturally use more channels if available. If
this explanation holds, then the multichannel/profitability relationship is one of reverse causality
– more profitable customers become multichannel rather than multichannel causes them to
become more profitable. The marketing explanation is that multichannel shoppers naturally
receive more marketing simply because they interact with the firm through several channels. For
example, the customer who utilizes the Internet and call center channels might be more likely to
receive both e-marketing and direct mail solicitations. The customer loyalty explanation views
multichannel marketing as additional service that increases customer satisfaction and ultimately
loyalty. The firm can increase service by making available the channel/s customers prefer and
providing tight integration between channels. In short, the multichannel customer is a happier
customer and therefore becomes more valuable. Another, more mechanical way of looking at it
is that the multichannel customer incurs a set-up cost (in learning how to use the various
channels) and hence would incur a switching cost in defecting to another company.
The multichannel-to-profitability link has received considerable empirical verification in the
literature. Both academic work and the business press suggest that multichannel buying behavior
equals extra profits (e.g. Loftus, Mulliken and Sharp 2008; Myers, Pickersgill, and Van Metre
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2004; Thomas and Sullivan 2005; Kumar and Venkatesan 2005; Venakatesan, Kumar and
Ravishanker 2007; Ansari, Mela, and Neslin 2008; Boehm 2008; Campbell and Frei 2010; Xue,
Hitt and Chen 2011). Thomas and Sullivan (2005) show that multichannel shoppers generate
more revenue, purchase more items, purchase in more categories, and purchase more frequently
than the single-channel shoppers counterpart. Venkatesan, Kumar, and Ravishanker (2007)
model customer profits versus lagged multichannel purchasing and their results show that
multichannel shopping is positively and significantly related to profits. Ansari, Mela, and Neslin
(2008) find a positive association between multichannel purchasing and customer revenues.
They attempt to disentangle the self-selection, loyalty, and marketing effects. They rule out the
first two and attribute the relationship to additional marketing and higher responsiveness to
marketing. Their findings highlight the need to control for other forms of marketing besides the
multichannel campaign we wish to assess. Indeed we will do so in that all customers in our field
test will receive the same marketing except for different multichannel campaigns.
While these studies are important for establishing the positive link between multichannel
buying and customer value, they do not address two issues: (1) Can a proactive marketing
campaign geared toward creating multichannel customers succeed in doing so, and (2) which
customers are most likely to respond to these efforts.
Can marketing create multichannel customers?
Previous research has investigated which factors influence customers’ choices of channels.
Blattberg, Kim, and Neslin (2008, Chapter 25) and Neslin et al. (2006) proposes six such factors:
(1) channel attributes, (2) social influence, (3) channel integration, (4) individual differences, (5)
situational factors, and (6) marketing.
Channel attributes include convenience, privacy, and risk. Social influence entails the
impact that significant “others” have on channel choice. Channel integration refers to
coordination among channels that makes it easier for the customer to use multiple channels.
Individual differences include demographics, psychographics, and previous channel usage
experience. Situational factors comprise the context of the purchase occasion, ranging from the
weather to the type of purchase (personal vs. gift). Finally, several studies have shown that
marketing can influence channel choice (Thomas and Sullivan 2005; Ansari, Mela, and Neslin
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2008), and the adoption of an additional channel (Venkatesan, Kumar, and Ravishanker 2007).
Marketing includes e-mails, catalogs, and incentives that encourage customers to use one
channel versus another. The marketing efforts investigated in these studies were not designed to
create multichannel customers. They do show however that marketing influences channel choice
at least of a particular channel. This suggests that marketing that attempts to convince customers
to become multichannel has a chance to succeed.
Interestingly, the above literature has primarily focused on non-financial communications
without considering the different effect that financial versus non-financial incentives might
generate on channel choice. Gedenk and Neslin (1999), for example, have shown that financial
incentives produce greater sales bumps but are relatively detrimental to brand loyalty, compared
to non-financial incentives.
In summary, previous research has found a positive association between multichannel
buying behavior and customer value. It has also found that marketing influences customer
choice of a particular channel, e.g., emails encourage Internet usage. This suggests that a
campaign to create multichannel usage has a chance to succeed. No previous research has
demonstrated that an actionable campaign could be assembled that: 1) creates multichannel
customers, 2) determines the most effective way to do this, and 3) examines the profit
implications of such a campaign. This is what we do.
Research Design
We are interested in determining whether the firm can increase its profit by turning its
customers into multichannel buyers using a proactively delivered marketing campaign. We also
want to investigate whether customers who are more likely to become multichannel can be
effectively targeted to improve profitability. In this section we first describe the setting for our
field test. Then we discuss the alternative campaigns we designed and the rationale behind them.
Next we describe the research design in detail. Finally we outline the analysis we will use to
measure the effectiveness of the campaigns and determine whether customers can be identified
who are particularly responsive to them.
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Empirical setting
We obtained the cooperation of a major multichannel European book retailer for conducting
the field experiment. The company sells books through stores, mail-order, phone and the
Internet. Each channel shares the same assortment and price.
The company operates on a subscription business model, thus each customer must become a
member in order to purchase, and all transactions and their timing are tracked. The firm sends its
main catalog five times per year, and its other marketing activities are managed around each
mailing, e.g., special promotions, price changes, etc. Consequently, customers make purchase
decisions in a shopping context created by the current catalog. In our data we monitor five
catalogue mailings (henceforth “periods”).
Importantly, none of the firm’s marketing activities is targeted according to channel usage.
This allows us to consider the firm’s current marketing activities as “baseline” and our field test
delivers additional different types of communications and incentives to drive multichannel
buying. Additionally, we benefited from a context in which newly acquired customers had never
been encouraged to change their channel choice pattern prior to the field test. The cooperating
firm never explicitly tried to move customers across channels, so the communications we sent in
our test was entirely new to these new customers.
Marketing communication treatments
We distinguish two key aspects of a marketing campaign designed to induce customers to
become multichannel: the message and the incentive. We will create a 2 × 2 design with
message at two levels and incentive at two levels. In specifying these levels, we wanted to cover
a range from highly explicit, overt urging of the customer to become multichannel, to a more
implicit, less overt “nudge.” We accomplished this as follows: The two levels of the message
factor are: (1) a “multichannel” message extolling the benefits of multichannel shopping and
making sure the customer is aware of the multichannel choices available, and (2) a “value
proposition” message emphasizing the key selling points of the company, which entailed
assortment, service, and special promotions. The multichannel message overtly urged the
customer to become multichannel. The value proposition message encouraged the customer to
buy more, which perhaps would result in the customer trying multiple channels.
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The two levels of the incentive factor were: financial incentives available versus not
available. The financial incentive was the provision of price discount coupons. While the
incentive factor is crossed with the message factor, the nature of the financial incentives differed
depending on the message. In the spirit of creating a “hard sell” multichannel campaign, the
financial incentives for the multichannel message entailed three coupons, one for each channel.
The idea was to provide direct incentive to use three channels, and a message that backed up
why this was a good idea for the customer. For the value proposition message, there were three
coupons but no specifications on which channels they could be used for. This was in the spirit of
creating a more soft-sell campaign.
In summary then, we have four campaigns identified by message (multichannel versus value
proposition), and incentive (financial versus non-financial). We label these
“multichannel/financial” (MF), “multichannel/non-financial” (MNF), “value
proposition/financial” (VPF), and “value proposition/non-financial” (VPNF). The campaign was
delivered via a prominent card that was sent just few days before the catalog mailing. A card
reminder was featured clearly at the front of the transparently wrapped catalog. Figure 1 shows
these four cards. (Figures and tables follow References throughout.)
As suggested above, the multichannel/financial campaign was the most explicit hard-sell
approach, while the value proposition/non-financial campaign was the most soft-sell. The
intermediate campaigns were also interesting. The multichannel/non-financial campaign
emphasized the message and perhaps would be a more effective communication. In terms of the
standard hierarchy of effects way of thinking about customer decisions, this campaign might be
most successful at creating the attitudes required for the customer to become multichannel.
However, it lacked the promotion aspect often necessary to convert attitudes to behavior. The
value proposition/financial campaign might convince the customer to buy more, but there is no
guidance here on becoming multichannel. That would presumably occur simply because the
customer would want to make more purchases, and learn in the process about multichannel
shopping.
We do not advance a priori hypotheses for the ordering of success for these campaigns. It is
tempting to anticipate the financial incentive campaigns will do better. They provide the
promotional push needed to change behavior. However, in the multichannel/financial campaign,
the coupons required multichannel behavior. The customer could react negatively to being
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forced to become multichannel, i.e., this approach might be too hard sell. In the value
proposition/financial treatment, the customer could simply use the coupons as needed for
purchases that would have been made anyway, hence little incrementality and little multichannel
shopping. Among the non-incentive campaigns, the multichannel message would appear to be
more effective for what we were trying to accomplish than simply reciting the value proposition
of the retailer. Presumably the customer was at least partially convinced of these virtues to begin
with, which is why the customer signed up with the retailer.
In summary, it is difficult to rationalize how the value proposition/non-financial campaign
would be best. However, arguments could be made for the other three campaigns. We leave it
to the field test to decide. What is important at this point is that we are demonstrating the need to
separate message from incentive, and think strategically about each of these in coming up with a
campaign to create multichannel customers.
Experimental design and data
The field experiment allows us to observe whether, and if so, which of the alternative
marketing campaigns induce multichannel buying, then examine whether this translates to higher
customer profitability.
We obtained two cohorts of customers (Cohort 1 and Cohort 2) who lived within at least one
store’s service area and who entered into a subscription agreement with the company after the
last catalog mailed in 2009 (Cohort 1) and 2010 (Cohort 2) was sent. We refer to the period in
which the customer entered into the subscription as the acquisition period; the latter periods are
post acquisition. For Cohort 1 the acquisition period was the fifth and last period of 2009; their
behavior was then monitored over the subsequent four periods in 2010. For Cohort 2, the
acquisition period was the fifth period of 2010; they were observed over the next five periods in
2011, from January 2011 until January 7, 2012. This means that this last cohort was followed for
five post-acquisition periods (see Figure 2). Cohort 1 was used to estimate a multichannel
potential model, described subsequently, and that is the only way in which their data are used.
Cohort 2 was the experimental cohort, randomly assigned to different marketing campaign
treatments.
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For Cohort 2 we created four treatment conditions (see Figure 3): multichannel/financial
(MF); value proposition/financial (VPF); multichannel/non-financial (MNF), and value
proposition/non-financial (VPNF), plus a control group.
We randomly selected customers to be included in different treatments and in the control
group. On January 7th, 2011, the beginning of period 1 for Cohort 2, customers included in the
treatment conditions received one of the above-mentioned cards one to three days before the
catalog was mailed to them. A card reminder was included in the catalog cover. By contrast,
customers allocated to the control group did not receive any communications except the catalog.
A second card was then sent using the same procedure, i.e. card then catalogue, on the 10th of
March, the beginning of Period 2, to the same customers. On May, 20; July, 29; and October, 7
respectively, a third, fourth, and fifth catalog was mailed to all customers both in treatment and
control conditions without any further communications related to channel usage. The firm
recorded all consumer transactions during these five periods. We therefore have information on:
which channel was selected by each customer on each purchase occasion, the date of each
purchase, and how much was spent.
Analysis
Analysis approach
There are four key parts to our analysis: First, we will estimate and evaluate a multichannel
potential model using data from Cohort 1 customers. This can be viewed as a database
marketing predictive model (Blattberg, Kim, and Neslin 2008) of whether a customer will
naturally become multichannel. We will use this model in two ways: (1) as a covariate for
enabling us to discern more clearly the impact of the treatments, and (2) as a variable to interact
with treatments to uncover whether response to treatment varies according to multichannel
potential. This will allow us to identify customers who should be targeted by specific treatments.
Second, we will provide simple descriptive statistics of the results - % who become
multichannel, average spending levels, etc. Third, we will use the multichannel potential model
to predict whether the customers in Cohort 2 are likely to become multichannel on their own, and
use these predictions as a covariate in analyzing the descriptive statistics and interacting these
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predictions with treatment. This will provide us with a more precise “read” on the impact of the
treatments, and generate guidance on whether there are interactions between treatment and
potential that could help in future targeting. Fourth, we will analyze the impact of becoming
multichannel on customer profits.
Steps 3 and 4 together tell us (1) which if any treatments created multichannel customers, (2)
which treatments should be targeted based on multichannel potential, and (3) whether
multichannel behavior translates into higher profits.
Multichannel potential model
As discussed above, we develop a predictive model to identify in advance customers with a
high probability of becoming multichannel customers. We estimate and test this model on
Cohort 1, and then use it to “score” Cohort 2 customers. This model is not of substantive interest
in and of itself. We simply want a model that performs well at predicting whether the customer
would or would not become multichannel on her/his own.
It is important to emphasize that the multichannel potential model requires only variables
obtained during the acquisition period, plus a time trend variable that is defined exogenously.
Therefore, the model can be applied to customers in Cohort 2 using only information available
by the end of their acquisition period, before they are assigned to treatments and observed.
Let i denote each customer (i=1,..., I), t denote time (t=1, …T), and Acquisition and Post
subscripts denote the acquisition versus post-acquisition periods. Customer i becomes
multichannel in the period in which for the first time she makes a purchase using a channel she
never used before. Becoming multichannel is therefore the “event” of interest. We consider the
five periods to be five discrete points in time because they correspond to the firm’s marketing
cycle of one catalog per period, and rarely did the customer make more than one purchase in a
period (only 7% of purchases).
Previous work has proposed two alternatives for defining multichannel shopping behavior.
One consists of cross-sectional ex-post measures whereby a customer who used more than one
channel or made a certain percentage (e.g. 80%) of her purchases using different channels at the
end of the observation period is considered a multichannel shopper (e.g. Kumar and Venkatesan
2005). The second alternative consists of time-varying measures where customers are defined as
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multichannel when they use more than a channel within a period t (i.e. a year, see Venkatesan,
Kumar and Ravishanker 2007). This measure varies over time, hence is able to capture the
dynamic nature of multichannel shopping, but it requires a minimum number of purchases per
period per customer. Customers need to have made at least two purchases in a given period in
two different channels to be classified as multichannel shoppers in that period. Moreover,
customers who make purchases across periods using two different channels are not considered as
multichannel.
Our definition combines the dynamics of the second approach with the more-than-onechannel aspect of the first approach. Specifically, we define the dependent variable to equal 1 in
the period t in which the customer i becomes multichannel, if indeed the customer ever becomes
multichannel1. Our definition focuses on the point in time when a customer moves from being a
single channel to a multiple channel user. Hence, it does not require a minimum number of
purchases per period. Customers who adopt a new channel are considered multichannel
regardless of their number of purchase occasions.
We use our dynamic measure of the multichannel behavior and model the probability the
customer becomes multichannel using a discrete-time hazard model formulated as a probit
regression function (Allison 1982). We can express, therefore, the potential of becoming
multichannel as:
(1)
Pr (Becoming Multichannel )Post ,it = Φ(X Acquisition ,i , Z Acquisition ,i , C Acquisition ,i , Trend t )
The symbol “Ф” stands for the cumulative distribution function of the standard normal
distribution. We used three classes of independent variables that can be observed during the
acquisition period (XAcquisition,i, ZAcquisition,i, and CAcquisition,i) and a trend term . XAcquisition,i indicates
observable individual variables available at the time the customer is acquired (e.g., gender, age,
place of residence, etc.). ZAcquisition,i indicates transactional variables that can be computed during
the acquisition period (e.g. RFM, returns). CAcquisition,i indicates the sales channel used by
customer i during the acquisition period (i.e. catalog, Internet, store or phone). Finally, we
1
For example if individual i became multichannel at time 3, we would have four different observations for this
individual. For the third observation, the multichannel dependent variable would be coded 1, and 0 for the previous
two time units. Cohort 1 is observed for a maximum of four time units, after the acquisition period. Hence if a
customer never experienced the event she would have four observations and the multichannel variable would be
coded as 0 for each of the four observations.
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include a Trendt term because the baseline hazard function may not be constant over time (see
Table A1 in the Appendix for the complete list of the variables used).
We estimated equation (1) with random effects across customers (see Table A2 in the
Appendix for results). We evaluated the predictive accuracy of this multichannel potential
model through cross-validation. Specifically we randomly select 75% (i.e. 26,583 customers) for
in-sample estimation and the remaining 25% (i.e. 8,808 customers) for out-sample prediction.
Figure 4 shows a lift chart for predicting which customers become multichannel. Predictions are
based on the random effect probit model parameter estimates. As the chart shows customers in
the top deciles have a distinctly higher chance of becoming multichannel customers compared to
the average customer. There is no degradation in lift in going from the in-sample to out-sample
predictions.
We conclude that this model produces good predictions of whether a customer will become
multichannel. The ultimate test of this, however, will be when we use its predictions as a
covariate in analyzing the results for Cohort 2. Specifically, we will use the parameter estimates
contained in Table A2 to predict each customer in Cohort 2’s potential to become multichannel.
We will only use the acquisition period data for this cohort, because that is all we need to predict
their natural propensity to become multichannel.
Descriptive analysis
We ran the field experiment for five periods encompassing a full year. During this time
customers purchased using the channel of their choice. We now present descriptive statistics of
the results. Figure 5 illustrates the cumulative percentage of customers who became
multichannel, by treatment, over the five periods. Table 1 provides more information (sample
size and standard errors) on the cumulative percent of customers who became multichannel by
the end of the entire observation period, by treatment. Figure 5 and Table 1 show that for the
multichannel/non-financial treatment there is a notable gain in the percentage of customers who
became multichannel. The percentages in Figure 5 and Table 1 are not significantly different at
conventional levels, but as described earlier, we will use the market potential model predictions
as a covariate in order to analyze these data with more precision.
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Figure 6 shows the difference in cumulative profits per customer by treatment versus the
control group. It appears the customers in the multichannel/non-financial treatment group
generate higher profit than the control group; the other treatment groups do not generate higher
profits compared to control. Table 2 reports the average cumulative profit over time, by group,
and also displays profit ROI. Only the multichannel non-financial communication group is
significantly different from the control group, and achieves a positive ROI (40%), whereas all
other conditions have a negative ROI.
To provide insight on why the multichannel/non-financial treatment performs best, we
examine the impact of the treatments on the frequency of purchase and the average profit per
purchase occasion. We observe a slight increase in the number of purchase occasions for
customers in the multichannel/non-financial treatment group by the end of the observation period
(Figure 7). More pronounced, customers who receive the multichannel/non-financial
communication cumulatively spend more per purchase occasion than customers included in other
groups (Figure 8). Coupled with the finding that these customers were more likely to become
multichannel over time, these results suggest that the multichannel message motivated customers
to explore the benefits of multichannel buying, and as they did so, they became more satisfied
customers and hence purchased more over time. Relatedly, the poor performance of the
multichannel/financial campaign results from its inability to motivate customers to explore
different channels. The multichannel/financial campaign forces customers to shop in channels
they might not like in order to take advantage of the financial offer. This can negatively affect
satisfaction with the shopping experience, hence does not have a beneficial effect on the amount
spent (see Figure 8).
The above descriptive statistics are consistent with our thesis that a marketing campaign can
be designed to turn customers multichannel and once they become multichannel they become
more profitable. However, we need more precision in order to verify statistical significance.
Accordingly, we utilize a statistical model with covariates to assess the magnitude and
significance of the campaigns’ effects on multichannel shopping and the impact of multichannel
shopping on profits.
Marketing Science Institute Working Paper Series
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6WDWLVWLFDODQDO\VLV
Our statistical analysis models the probability of a customer becoming multichannel, and the
change in customers’ profits from this time point onward. The profits equation will contain a
dummy variable signifying whether the customer becomes multichannel. However, becoming
multichannel is endogenous. Therefore, we need to take into account the potential endogeneity
of the multichannel dummy (see e.g., Imbens and Angrist 1994; Angrist, Imbens and Rubin
1996; Wooldrige 2002, Chapter 18; Verbeek 2004, Chapter 7). To do so, we formulate an
endogenous binary variable model (becoming multichannel) to examine the impact of becoming
multichannel on profits.
Let i indicate the individual (i=1, …I), t indicates the time (t=1,…5), and j the treatment
(j=1,…4). Equations 2 and 3 summarize the specification of this model:
(2)
(3)
4
4
j =1
j =1
Multichannel *it = ιi + α t + δPotentialit + ∑ λ jTreatmentit + ∑ κ jTreatmentij × Potentialit + ν it
Profitit = γ 0i + βMultichannelit + γ 1Seasonalityt + γ 2Trend t + ε it
Where:
1 if
Mulichannelit = 
0 if
Multichannel * it
Multichannel * it
>0
≤0
And
Profitit
= Profit (€) generated by customer i during time unit t.
Multichannelit = Dummy variable that indicates the event of becoming multichannel.
This variable takes value 1 if customer i became multichannel in time
unit t, and 0 otherwise.
Seasonalityt
= Seasonal index for revenues in time unit t (we adapted the Abraham and
Lodish’s (1993) method to this context to derive this index).
Trendt
= Linear trend term.
Potentialit
= Probability of becoming multichannel. We used the parameter estimates
of the multichannel potential model described in equation (1) to predict
this probability.
Marketing Science Institute Working Paper Series
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Treatmentij
= dummy variables that take value 1 if customer i is in the treatement
group j (j=1,…,4), and 0 otherwise. The four groups (MF, VPF, MNF
VPNF) identify the different types of campaigns used. The control
group (i.e. no campaign) represents the baseline.
In the above specification, Multichannelit is the endogenous dummy variable indicating
whether the customer becomes multichannel or not. Maddala (1983, p.120-121) derives a twostep estimator procedure in the form of a switching regression model:
(4)
E ( Profit it | Mutltichannelit = 1 ) = β + γ 1 Sesonalityt + γ 2Trend t
4
φ (α t + δPotentialit + ∑ λ j Incentiveij +
+σ
j =1
4
Φ (α t + δPotentialit + ∑ λ j Incentiveij +
j =1
(5)
4
∑κ
j
Incentiveij × Potentialit )
j
Incentiveij × Potentialit )
j =1
4
∑κ
j =1
E ( Profit it | Mutltichannelit = 0 ) = γ 1 Sesonalityt + γ 2Trend t
4
φ (α t + δPotentialit + ∑ λ j Incentiveij +
−σ
j =1
4
∑κ
j
Incentiveij × Potentialit )
j =1
4
1 − Φ (α t + δPotentialit + ∑ λ j Incentiveij +
j =1
4
∑κ
j
Incentiveij × Potentialit )
j =1
Where σ=Cov(εht,νht), and ϕ and Ф stand for the density cumulative distribution functions of
the standard normal distribution. The key parameters of interest in these equations are: β which
quantifies the impact of the treatment endogenous variable (becoming multichannel) on the
outcome variable (profits), δ which indicates the role played by the individual pre-disposition of
becoming multichannel, λj which captures the effectiveness of treatments in turning customers
multichannel, and κj, which reports whether the effectiveness of the treatments is moderated by
the potential to become multichannel. Finally, αt is a set of constants to express the hazard rate
as a function of time (Allison 1982). To estimate these parameters we used a random effect
probit for the multichannel model, a random effect GLS regression for the profit equation, and
the two-stage estimation procedure proposed by Maddala (1983, p.121).
Statistical analysis results
Table 3 shows the estimation results of the multichannel model (equation 3) and compares
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three different nested versions with the full model. Model A represents the basic model. It is
just a function of treatments and potential. Model B adds interactions between potential and
treatments. Model C relaxes the assumption of a constant hazard rate over time. The full model
is a function of potential, treatments, interactions between potential and treatments, and time.
Models A-C are nested within the full model. We compared each alternative nested model with
the full model using a likelihood ratio test. The LR test statistic indicates that the more general
specification fits the data significantly better than the alternative nested specifications (model AC).
We summarize the results in Table 3:
Time. The set of constants that express the hazard rate as a function of time are significant
and indicate that the baseline hazard is not constant over time. No clear time pattern emerges,
although it is less likely to become multichannel immediately after the acquisition period (α1= 4.19), then there is a tendency of the hazard rate to increase over time.
Treatments. One of the key objectives of this work is to investigate whether a firm can
create more multichannel customers. Our results indicate that the multichannel/non-financial
campaign (MNF) that communicated the benefits of multichannel shopping but did not offer a
financial incentive is the only campaign that achieves this (λ3=0.10). The impact of this
campaign on multichannel buying behavior is significant at p < 0.05.
Potential: Results show that indeed pre-disposition towards multichannel usage (potential)
increases the probability of becoming multichannel (δ=5.89). Note this means that companies
can identify in advance customers with different levels of pre-disposition through a targeting
model by using just the information observable at the very start of the relationship with its
customer base (i.e. the acquisition period).
Interaction between treatments and potential: Customers can be more or less receptive to a
given multichannel campaigns depending on how strong their pre-disposition is towards
multichannel shopping. The superior performance of Model D indicates that these interactions
add explanatory power. To investigate the nature of these interactions in more detail we
computed for each customer the slope of the predicted effect of each incentive on the probability
of becoming multichannel at any value of potential, and then we computed the estimated
Marketing Science Institute Working Paper Series
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standard error for each effect2. The significance test of the impact of the incentives at any given
value of potential takes the form of a t-test that divides the coefficient by its estimated standard
error (Jaccard and Turrisi 2003).
Figure 9 plots on the y-axis the t-test of the impact of the incentive and on the x-axis
different values of potential3. This figure summarizes the impact of each treatment at different
possible levels of pre-disposition towards multichannel behavior. It shows that at low values of
potential the multichannel/non-financial treatment (MNF) has a positive and significant impact
on becoming multichannel, but for very high level of potential the magnitude of the impact
decreases and the treatment does not significantly affect the probability. Value proposition
treatments (both financial and non-financial) do not significantly affect the probability of
multichannel shopping at any level of potential. By contrast, the multichannel/financial
treatment at high levels of potential is slightly significant, with a negative impact, i.e., it
decreases the probability of becoming multichannel among customers with a high potential of
becoming multichannel.
In summary, these results show that a marketing campaign can trigger multichannel
shopping. The multichannel message coupled with no financial incentive (MNF) achieves this,
especially for customers with a very low a priori propensity of becoming multichannel. We
therefore computed ROI assuming that the firm mails the card with the multichannel/nonfinancial message just to those customers for whom the impact of this treatment is significant
(i.e. t-stat >1.96), as the company would do in implementing the program in a new cohort of
customers or in a second year. The ROI not surprisingly increases to 51%. By contrast, the
financial campaigns not only do not work, but may produce a negative effect in terms of
multichannel shopping among customers with a high pre-disposition towards the multichannel
usage, who maybe would have become multichannel spontaneously if financial incentives had
not been offered.
Note that in our cohort of customers the potential prediction assumes on average a value of
0.07 (Standard Deviation= .098, Median=0.04). This means that the multichannel/non-financial
campaign has a positive and significant effect for the majority of the customer base. This
2
bj at Potential=λj+κjPotential
SE(bj at Potential)=[(var(λj)+Potential2var(κj)+2Potentialcov(λj, κj)]1/2 , where j=1 if MF, j=2 if VPF, j=3 if MNF, j=4
if VPNF.
3
Note that potential is a probability, hence it varies from 0 to 1. We built the chart by taking into consideration all
the 0.01 increments starting from 0 till 1.
Marketing Science Institute Working Paper Series
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treatment is not effective just for the customers included in the top-decile of the variable
potential. In contrast, the multichannel/financial treatment has a significant and negative impact
just for the customers with an extremely high pre-disposition towards multichannel usage, that is,
customers included in the top decile in Cohort 2.
Multichannel shopping and Profits. We estimated equation (3) to examine the link between
multichannel shopping and customer profitability, answering the question of whether newly
created multichannel customers become more valuable to the firm. Table 4 shows that
multichannel shopping has a positive and significant impact (β=8.45). Hence, when the
customer becomes multichannel she generates on average an additional profit of 8.45 € per
period. Note that the coefficient that represents the covariance between the error terms of the
multichannel and profit models is not significant (σ= Cov(εht,νht) =-0.05). This means that the
correlation between the error terms of equation (2) and (3) is close to zero and seems to suggest
that individuals do not self-select into the becoming multichannel on the basis of the
idiosyncratic component of their response to the treatment (Verbeek 2004). This might suggest
that the “self-selection” explanation provided to explain the higher purchase volumes observed
among multichannel customers (see Blattberg, Kim and Neslin 2008, p. 639) is not supported.
Overall these results confirm the thesis of our research, that a marketing campaign can be
designed that produces more multichannel customers, and in turn these customers are more
profitable. We show that the particular campaign that does this – a benefits-of-multichannel
message not coupled with a financial incentive – works especially well on customers who a
priori would not have been expected to become multichannel on their own. This is a sensible
result and suggests that while the campaign is profitable when mailed indiscriminately to a group
of customers, it can be made even more profitable (on a ROI basis) by targeting customers who
otherwise have low potential of becoming multichannel.
Conclusions
This research sheds light on the relationship between multichannel shopping and
profitability. Previous works provided some empirical evidence of their association (e.g.
Venkatesan, Kumar, and Ravishanker 2007; Hitt and Frei 2002). In this paper, we show this
association can be translated into practice by designing a marketing campaign that produces
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more multichannel customers who then are more profitable. We thus establish a causal link from
marketing to multichannel to profitability.
Our work provides insights into how to design more effective marketing campaigns to
induce customers to become multichannel. Customers are responsive to specific claims
emphasizing the advantages of multichannel shopping. By inducing customers to explore the
benefits of multichannel buying, the non-financial multichannel message makes customers more
inclined to spend. These findings corroborate existing literature documenting the positive effect
of non-financial communication in multichannel contexts (e.g. Ansari, Mela and Neslin 2008).
Moreover, we find that customers are not responsive to a firm’s value proposition message or to
financial incentives.
Our field experiment allows for the measurement of the impact of customers’ turning
multichannel on profit. Again we show that a multichannel/non-financial campaign reminding
customers of the availability of a firm’s different points of contact is inexpensive and effective at
both generating multichannel shoppers and increasing profits. This strategy acts to modify
customers’ channel shopping behavior at a lower cost than financial multichannel incentives.
The multichannel shoppers who responded to this campaign generate more profit and are
therefore more valuable to the firm.
Our field experiment is a longitudinal study. We exposed customers to different treatments
for two periods and we monitor them for three periods after the campaign finished. This allowed
us to assess how persistent is the effect of the communication treatments on a customer’s
likelihood to turn multichannel and in turn on customer profitability in the long-run.
This research also shows that customers with a high pre-disposition towards multichannel
shopping have little “upside”. For instance, the multichannel/non-financial communication is
more effective on low-potential customers, and not effective on high-potential customers. This
implies that marketing campaigns should be pre-tested to develop targeting strategy for reaching
customers who are more likely to respond to this campaign.
Our field test offers some important results for managers. First and foremost, the
multichannel customer strategy is viable. Firms can devise marketing campaigns turning
customers into multichannel shoppers and making them more profitable. Second, not all
communications and incentives are equally effective. Non-price oriented promotional
advertising increases the likelihood and accelerates the time to become multichannel. More
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specifically multichannel non-financial communications increase revenues by 1.35 € per year at
a cost of 0.46 (the cost of two cards) euros per customer. This is a ROI of 40%. Second financial
incentives might be a loss-generating activity for firms because they are not only costly and
ineffective for the large majority of the customer based, but they can even negatively affect the
probability of using multiple channels of customers favorably disposed to multichannel shopping
(high potential).
Several factors could influence the effectives of marketing campaigns to encourage
multichannel shopping behavior beside the nature of the incentives including: frequency, copy,
scheduling. Additionally, the industry in which a firm operates along with the products it sells
may influence our results. We, therefore, recommend that managers test their campaign before
implementing it.
Finally, we show that a model of multichannel potential can be used to increase ROI through
targeting, because customers with low a priori probabilities of becoming multichannel are the
ones who are most responsive to the optimal campaign.
Limitations and Future Research
There are several limitations to our approach that invite further research and extensions.
First we use data from a single company and from a single market. It will be useful to replicate
this work in different contexts where more firms are examined; customers are not bounded to the
firm by a contract, and/or buy more frequently.
Second, while the best campaign is profitable, the percentage of customers who eventually
turned multichannel is rather low. This might be related to the low number of purchase
occasions observed in this industry, which limit a customer’s opportunity to use multiple
channels.
Third, we just observed whether customers made a purchase and we do not know whether
our treatments encouraged customer to search across channels. Future work could pursue this
avenue and try to assess whether marketing campaigns boost channel search and make customers
more satisfied with the shopping experience.
Fourth, our treatments were sent to customers immediately after the acquisition period (first
and second periods). In this way, campaigns reached customers new-to-the-firm, who had never
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being encouraged to change their channel usage before the field test, so did not have time to form
channel preferences with this firm and were still responsive to marketing (Valentini, Montaguti
and Neslin 2011). However, during the acquisition phase, in order to attract new customers the
cooperating firm offers several price and quantity discounts. This generally affects the frequency
of purchase in the first post-acquisition period and might have reduced the number of
multichannel customers in the first period.
In summary, this research builds the knowledge base in this critical area by demonstrating
the effectiveness of multichannel campaigns, offering guidance on the design of those
campaigns, and demonstrating the potential for targeting based on a priori estimated models of
the customer’s multichannel potential. This clearly demonstrates the causal link from marketing
actions to multichannel customer behavior to customer profitability.
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Table 1: Percentage of Customers Who Become Multichannel by the End of the
Observation Period, by Treatment Group
Treatment
Groups
MF
VPF
MNF
VPNF
Control
Total
n
Percent
7063
7074
7068
7042
3533
31780
SE
7.7%
7.6%
8.4%
7.6%
7.5%
7.8%
Marketing Science Institute Working Paper Series
.3%
.3%
.3%
.3%
.4%
.2%
26
Table 2: Average Cumulative Profits per Customer as of the Last Observation Period
p-value a
Average
Cumulative Profits
n
ROIb
7063
.665
MF
€ 17.50
-142%
7074
.515
VPF
€ 17.68
-104%
7068
.078
MNF
€ 18.34
40%
7042
.524
VPNF
€ 17.67
-106%
3533
Control
€ 17.69
a
Two sample t-test: we tested the difference between the average cumulative profit of each
treatment and the average cumulative profit of the control group.
b
ROI= [(Unit ProfitTreatment- Unit ProfitControl) – Treatment Cards Cost]/Treatment Cards Cost
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Table 3: Estimates for Random Effects Probit Models Predicting the Probability of
Becoming a Multichannel Shopper.
Variable
MF (λ1)
VPF (λ2)
MNF (λ3)
VPNF (λ4)
Model B:
Model A: Basic Basic Model +
Model
Interactions
.01
.03
Model C:
Basic Model
+αt
.03
Model D:
Full Model
.05
(.03)
(.03)
(.05)
(.05)
.01
.01
.01
.03
(.03)
(.03)
(.05)
(.05)
.05*
.09*
.10**
(.05)
(.05)
.01
.06*
(.03)
.02
.02
.04
(.03)
(.03)
(.05)
(.03)
***
Potential (δ)
MF*Potential (κ1)
VPF*Potential (κ2)
MNF*Potential (κ3)
VPNF*Potential (κ4)
α1
α2
α3
α4
α5
Constant (α)
Observations
Log likelihood
Likelihood-ratio test of
nested vs. full model
*
p<.10, ** p<.05, ***p<.01
2.81
3.25
(.19)
(.39)
***
5.17
(.05)
***
(.11)
(.72)
-.76*
-
(.43)
-1.39*
-
(.81)
-.69
-.39
-
(.43)
-
(.81)
-.21
-
(.43)
-.23
-
(.81)
-.59
-
(.43)
-
-
-.91
-4.18***
-4.19***
(.17)
(.17)
-
-
-
-
-
(.81)
***
-3.02***
(.12)
(.12)
-2.98***
-2.99***
-3.03
-
5.89***
(.10)
(.11)
-3.06***
-3.07***
(.10)
(.10)
-3.04***
-3.05***
-2.23***
-2.22***
(.10)
(.10)
(.05)
(.03)
-
-
154455
-12445.9
154455
-12443.6
χ2(9)=689.67*** χ2(5)=685.04***
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154455
-12105.9
χ2(4)=9.58**
154455
-12101.1
-
28
Table 4: Estimates for Random Effects GLS Regression Predicting Profits
Variable
Coef
Seasonal (γ1)
7.43***
Trend (γ2)
-1.31***
Covariance between the
-.05
error terms (σ)
Multichannel impact (β)
8.45***
Dependent Variable: Profitit
Sample (Multichannel=1) Observations:= 6255
Wald χ2(3) =219.28***
*
p<.10, ** p<.05, ***p<.01
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SE
.80
.10
p-value
.00
.00
.27
.86
1.22
.00
29
Figure 1: Marketing Communication Treatments
Financial Multichannel Incentive (MF)
Financial Value Proposition Incentive (VPF)
Non-Financial Multichannel Message (MNF)
Non-Financial Value Proposition Message (VPNF)
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Figure 2: Experimental Design-Timeline
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Figure 3: Test Conditions
Multichannel/
Financial
(MF)
Group MF:
Multichannel Financial Group
7,064 customers
Value
Proposition/
Financial
(VPF)
Group VPF:
Value Proposition Financial
Group
7,074 customers
Multichannel/
Non-Financial
(MNF)
Group MNF:
Multichannel Non-Financial
Group
7,068 customers
Value
Proposition/
Non-Financial
(VPNF)
Group VPNF:
Value Proposition NonFinancial Group
7,043 customers
No-Campaign
Control
Control Group
No-Campaign Control Group
3,534 customers
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Figure 4: Lift Chart Performance of Multichannel Potential Model – Cohort 1
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Figure 5: Cumulative Percentage of Customers Who Became Multichannel in Each Period,
by Treatment
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Figure 6: Difference in Cumulative Profits per Customer vs. Control Group - Full Sample
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Figure 7: Differences in Cumulative Average # of Purchase Occasions per Customer vs.
Control Group at the End of the Observation Period
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Figure 8: Difference in Average Cumulative Revenues per Purchase Occasion per
Customer vs. Control Group at the End of the Observation Period
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Figure 9: Significance of Treatment Effects at Different Values of Multichannel Potential
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APPENDIX
Table A1: Independent Variables Used in the Multichannel Potential Model
Class
Variable
Description
Age of the customer
XAcquisition,h Age
Female
North
Big City
Average City
Early Email
Franchisee
Street Agent
Nov Acquisition
Dec Acquisition
CAcquisition,h
Initial Catalog
Initial Web
Initial Store
Initial Phone
ZAcquisition,h
Initial Store Promo
Initial Returns
Initial Price Cut
Initial Revenues
Initial Purchase
Trendt
Trend
Dummy variable that takes value 1 if the customer is a female
and 0 otherwise
Dummy variable that takes value 1 if the customer lives in the
north of the country, and 0 otherwise
Dummy variable that takes value 1 if the customer lives in a big
city (more than 500 thousand inhabitants), and 0 otherwise
Dummy variable that takes value 1 if the customer lives in an
average city (499 – 100 thousand inhabitants), and 0 otherwise
Dummy variable that takes value 1 if the customer provided her
email address during the acquisition quarter, and 0 otherwise
Dummy variable that takes value 1 if the store closest to the
customer place of residence is run by a franchisee, and 0 if the
closest store is run directly by the firm.
Dummy variable that takes value 1 if the customer was acquired
through an on-the-street agent, and 0 otherwise
Dummy variable that takes value 1 if the customer was acquired
in November, and 0 otherwise
Dummy variable that takes value 1 if the customer was acquired
in December, and 0 otherwise
Dummy variable that takes value 1 if the customer purchased
through the catalog during the acquisition time unit and 0
otherwise
Dummy variable that takes value 1 if the customer purchased
through the Internet during the acquisition time unit and 0
otherwise
Dummy variable that takes value 1 if the customer purchased
through the store during the acquisition time unit and 0
otherwise
Dummy variable that takes value 1 if the customer purchased
through the phone during the acquisition time unit and 0
otherwise
Dummy variable that takes value 1 if in the closest store was
running special store promotions, and 0 otherwise
Total value (€) of products returned to the firm by the customer i
in the acquisition period.
Total value (€) of price discounts used by the customer i in the
acquisition period
Total amount (€) spent during the acquisition quarter
Dummy variable that takes value 1 if the customer purchased
during the acquisition quarter, and 0 otherwise
Linear trend term
Marketing Science Institute Working Paper Series
39
Table A2: Estimates for Random Effects Probit Model Predicting the Probability of
Becoming a Multichannel Shopper – Multichannel Potential Model
Variable
Age
Female
Street Agent
North
Early Email
Nov Acquisition
Dec Acquisition
Big City
Average City
Franchisee
Initial Catalog
Initial Web
Initial Store
Initial Phone
Initial Store Promo
Initial Returns
Initial Price Cut
Initial Revenues
Initial Purchase
Trend
Constant
Coef.
.004
.111
-.156
.231
.242
.287
.386
-.081
-.102
-.041
2.198
2.046
.815
1.672
-.053
-.010
-.010
.002
-.475
.092
-3.661
Marketing Science Institute Working Paper Series
SE
.001
.027
.027
.034
.030
.034
.043
.044
.048
.025
.138
.132
.086
.114
.042
.003
.003
.001
.084
.012
.144
p-value
.000
.000
.000
.000
.000
.000
.000
.065
.035
.096
.000
.000
.000
.000
.209
.001
.000
.152
.000
.000
.000
40