1 Toward a Sustainable Email Marketing ...

Forthcoming in the Journal of Business Research (2008)

Toward a Sustainable Email Marketing Infrastructure

Oleg Pavlov1 Nigel Melville Robert Plice

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Corresponding author. Oleg V. Pavlov is Assistant Professor of Economics and System Dynamics, Worcester Polytechnic Institute, USA, [email protected]. Nigel Melville is Assistant Professor of Business Information Technology, Stephen M. Ross School of Business, University of Michigan, Ann Arbor, [email protected]. Robert Plice is Assistant Professor of Information and Decision Systems, San Diego State University, [email protected]. The authors acknowledge the helpful comments and suggestions of participants in the 13th International Conference on Computing in Economics and Finance, Montreal, Canada, June 14-16, 2007 and 25th International Conference of the System Dynamics Society, Boston, MA, July 29- August 2, 2007

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Abstract Email marketing is a legitimate, lucrative, and widely used business tool, but it is in danger of being overrun by unwanted commercial email (also known as spam). Conventional approaches to maintaining the robustness of legitimate email attack pieces of the problem. In contrast, this article asserts that the email marketing infrastructure is a complex system requiring holistic analysis. A system dynamics model is developed to identify the underlying dynamics and enable an examination of alternative mitigation strategies. Results reveal that the system conforms with the limits-to-growth generic structure, filtering may have the unintended consequence of increasing the global amount of spam, and the unexpected increase comes about because better filters can actually assist spammers by mitigating an information deficit.

Keywords: Email, marketing, spam, system dynamics

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1. Introduction The Internet offers a new paradigm for marketing, engendering a shift from product to customer focus that includes micro-level customization and customer relationship management (Rust and Espinoza, 2006). An example is search-based advertising, which grew from virtually nothing in the early 1990s to a $14 billion industry in 2006 (Elkin, 2007). Another example is email marketing, which provides twice the return on investment (ROI) relative to other forms of online marketing: $57.25 for each dollar spent versus $22.52 (Direct Marketing Association). Email marketing is now employed by 70% of all retailers and growing 10% annually (Mccloskey, 2006). Ironically, email marketing may be its own worst enemy. The Internet drastically lowers communication costs – an underlying driver of high email marketing ROI relative to other channels. In turn, low production costs spur greater production, inducing entry to the industry by legitimate and not-so-legitimate marketers, further increasing the volume of email messages sent. As a result, consumers are awash in a sea of ads and information, some useful and some not. Knowledge workers sift through hundreds of such emails per week, more than one-half of which are spam, leading to information overload – a situation when more information is not better (Schwartz, 2004; Simon, 1971). The bottom line is that useful email marketing messages are lost in the background noise with negative consequences for short-term ROI and long-term industry health. The spiraling situation of email marketing overload and its deleterious impact on ROI has been acknowledged by the industry, for example: “I recommend we all keep our ears to the ground on this and work through organizations like the Email Sender and Provider Coalition to help find balanced solutions that stop spam, but allow businesses to continue

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to use the email medium for legitimate, permission-based communications.” (Nussey, 2007). The fundamental thesis of this article is that the email marketing infrastructure is a complex adaptive system whose behavior is best understood by examining the system as a whole, rather than isolated components (Kofman and Senge, 1993;Woodside, 2006). The limitations of applying reductionist thinking to social, technical, and biological systems is well documented (Senge, 1990), and exacerbated by inherent human blind spots to the effects of dynamics and feedback (Moxnes, 1998). As explicated below, such limitations have impeded attempts to stem the tide of spam and maintain a robust email marketing infrastructure. Email marketing is analyzed in this article using system dynamics, a method that embodies the logic of feedbacks and employs computational models to simulate dynamic behavior. Results of simulations are presented in graphical form, such as volume of sent spam versus time. A post-simulation analysis in terms of feedback effects leads to a better understanding of the underlying drivers of the behavior of the email system. The method is thus consistent with the twin objectives of raising awareness of the hidden dynamics that impact industry health and examining the effectiveness of various mitigation strategies. The system dynamics analysis undertaken in this article reveals several mechanisms that explain key phenomena associated with the email marketing infrastructure. The first mechanism is that limited human attention acts as a limit to growth of the email marketing system. The second mechanism is that filters have an unintended consequence. That is, filters lead to fewer emails in user inboxes but much

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greater volumes of spam in the email system as a whole. Finally, this unintended consequence of filtering is explained in terms of the spam targeting mechanism: filters can be viewed as a proxy for an information resource that spammers lack, enabling spammers to achieve the effect of message targeting. The next section includes a review of prior research examining email marketing and spam, revealing that most approaches involve a focus on one particular aspect of the email marketing system, rather than analysis of the system as a whole. It also provides an overview of system dynamics methodology. Then, an analysis of the behavior of a developed system dynamics model reveals causal mechanisms that explain underlying dynamics. Concluding remarks discuss implications for marketing professionals and policy markers.

2. Approaches to Analyzing Email Marketing Prior Research Prior research on email marketing and spam can broadly be classified into two groups. The first includes studies that focus specifically at reducing spam from a broad range of perspectives. The second includes studies from the marketing literature that examine determinants of response rates for email marketing campaigns. Most studies of email marketing and spam reduce the problem to one or two core components, analyze those components, and make recommendations based on that analysis. A frequent topic has been the development of more efficient algorithms for distinguishing regular email from spam and filtering it (Fawcett, 2003; Gray and Haahr, 2004). Filtering techniques are one of the most popular topics of the two major conferences on spam, the Conference on Email and Anti-Spam (CEAS) and the MIT

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Spam Conference. To enhance filtering, email providers maintain lists of computers from which they do not accept any email, the so-called blackhole lists (Goodman et al., 2005). There is also a movement to augment filters by maintaining lists of “good” senders – email from any computer that is not on the list will be rejected on the premise that it is spam (Goodman et al., 2005). Another vigorous area of research examines the regulatory environment and the impact of legislation on email marketing and spam (Goldman, 2006; Gratton, 2004). Here the thinking is that laws can constrain the type of email marketing that will be sent, and so can be designed to allow “good” email marketing and eliminate “bad” email marketing. But, the CAN-SPAM Act of 2003 in the U.S. does not so far appear to have stopped the flood of spam (although a number of legal cases have been brought, potentially resulting in a signaling mechanism to less-than-legitimate operators). Beyond filtering and legal mechanisms, other approaches include reducing email overload by aiding recipients with information processing, specifically, presorting arriving email (Croson et al., 2005). Similarly, another proposal involves innovative use of a filter as part of a dynamic pricing mechanism, where price is determined proportionally to the filter score assigned to the message by a filter (Dai and Li, 2004). Increasing the cost of sending email may alter the economics of spam toward enhanced overall efficiency (Goodman et al., 2005). In particular, email service providers may impose a reverse Turing test (to test that the sender is human) and require that computers of spammers solve difficult computations in order to increase the cost of sending spam. Researchers have also employed welfare economics to examine the distribution of surplus between sender and receiver based on whether a message has value to a given

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receiver (Loder et al., 2006). One analysis uses game theoretic approaches to analyze the two-agent non-cooperative game between sender and receiver, finding a unique Nash equilibrium and enabling prediction of the optimal point in the tradeoff between Type I and Type II filter errors (Androutsopoulos et al., 2005). Another study demonstrates the potential of systems thinking by developing an exploratory model of email communication, suggesting that this may be a very fruitful approach (Pavlov et al., 2005). Several marketing research studies concentrate on predicting response rates, such as for catalog mailing (Basu et al., 1995). Recent studies have looked specifically at email communication. For example, a model of online clicking behavior attempts to predict and improve response rates for email communications (Ansari and Mela, 2003). In this study, the focus is on learning how to use individual preferences to custom-design marketing emails, including aspects of content inclusion and presentation design. The model accounts for the heterogeneity in preferences and the existence of some unobservable variables. Customization is found to be very desirable, but not easily achievable. There are also companies that specialize in email customization for commercial clients, including Yesmail and DoubleClick. Overall, marketing studies tend to be narrowly focused on manipulating specific, short-term behaviors with the goal of improving return on investment. In sum, extant research has improved the effectiveness of filters, even though their deployment has not seemed to stem the flow of spam. Prior research has also helped inform regulatory mechanisms, given rise to proposals for new methods of spam mitigation, and demonstrated the viability and usefulness of systems thinking applied to

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the spam problem. To complement and extend existing studies, we model email marketing as a socio-economic system and examine how the system behaves and responds to two specific policies. Achieving such insights requires that we take a systems approach, viewing the phenomenon as a whole rather than in terms of its isolated components. The analytical tools of system dynamics are particularly appropriate for such an investigation. System Dynamics Methodology Developing a system dynamics model involves a series of five steps (Randers, 1980). First is defining the system boundary appropriate for the research problem. This step is typically accomplished by drawing on observation, prior research, and knowledge elicitation from experts. Based on the accounts of the spam industry (e.g. Mcwilliams, 2005), we set the boundary of our model to include the following economic actors: senders of email (broadcasters), sponsors of commercial messages, recipients of email, and technology firms that provide filtering products. The second step is defining variables of the system. In our case, variables include volumes of spam and regular email sent by the respective types of broadcasters, the stock of messages in the backlog, the attention endowment which measures the processing capacity of the recipients, the revenue paid to sponsors of commercial messages, and filter quality in terms of error rates, such as false-positives and false-negatives. Third, reference modes of the system are explicated, that is, how individuals and organizations think and react within the system over time. For example, what actions do recipients take when their daily attention endowment, measured in terms of time, is exhausted? The fourth step prior to building the model is to embody relationships among variables in causal diagrams that may

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involve causal links and feedback loops. Based on these preliminary steps, the simulation model is developed and validated in the fifth step, and policy recommendations are suggested based on dynamic behavior of the system.

3. Causal Mechanisms in Email Marketing System Systems thinking and the model development steps outlined above motivate the construction of a system dynamics model of email marketing. We now explain the key causal mechanisms of the email system and the baseline dynamic behavior they generate. We then analyze an unintended consequence of filtering and compare filtering to message targeting. Attention as a Limiting Condition Conventional marketing research on email communication and spam examines individual variables while acknowledging some simple causality between the variables. The email marketing context is viewed as another advertising medium with differences in key characteristics relative to print media: it is cheaper, information intensity is higher, personalization is easier, and so forth. The causal logic is that more and better volumes of email marketing lead to desired consumer actions. This view of email marketing is overly simplistic. Email is part of a complex social system that is stimulated by economic forces and constrained by cognitive limitations of the receiving side. A schematic view of the situation is in Figure 1. The diagram shows some key variables, and arrows indicate the causality between the variables. Positive arrows imply a positive relationship between variables and negative arrows show that variables move in the opposite directions. Thinking in terms of systems reveals two primary feedback effects: the reinforcing loop

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leading to a wealth of information and the balancing loop leading to a poverty of attention. These loops are marked by letters R (for reinforcing) and B (for balancing) respectively. While the reinforcing loop comprises the supply of spam, the balancing loop acts to quell the growth of messages. The growth process of spam volume is driven by message broadcasters and their sponsors. Suppliers of commercial email messages – whether legitimate newsletters from a consumer’s bank, legitimate but unsolicited messages from online shoe stores, or illegitimate messages intended to defraud – produce and send messages to email recipients. Spam filters analyze arriving email and discard messages which have been identified as non-valuable. However, due to the false-negative error by the filter (Cormack, 2006), a certain percent of unwanted spam messages is not identified as spam, and therefore not discarded but added to the message backlog. A filter that commits fewer false-negative errors allows fewer unwanted spam messages through, and thus improves the average fraction of valuable spam messages in the backlog. The model includes the percent of false-negatives as an exogenous policy parameter. When the filter misidentifies a valuable message as spam, a false-positive error is committed (Cormack, 2006). False-positives have a negative effect on the average fraction of valuable spam messages in the backlog. Another exogenous policy parameter is the fraction of valuable spam messages. The parameter represents the targeting effort by the email marketers. Ideally, the spammers would send only messages which are of interest to recipients, thus achieving perfect targeting. In the situation of perfect targeting, the fraction of valuable spam

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messages is one. The fraction of valuable spam messages is positively related to the average fraction of valuable spam messages in the backlog. Spam and regular messages are added to the message backlog. Message backlog is a stock variable, which is indicated by a rectangle around the variable name. Messages can be either processed or deleted. The size of the message backlog and the average fraction of valuable spam messages in the backlog determine how many valuable spam messages can be potentially processed by the recipients. Recipients spend some daily time endowment reading messages in the message backlog. Hence, the number of valuable spam messages processed daily depends on the daily time endowment, the average fraction of valuable spam messages, and the size of the message backlog. Each of these three determinants has a positive effect on the number of valuable spam messages processed (Figure 1). We assume that when a valuable commercial message is processed, it generates a sale (otherwise the message would not be considered valuable) and the sponsor receives revenue. Assuming that spam funding, which is a stock, is positively related to the revenue generated by spam, we connect revenue and spam funding with a positive arrow. More spam funding means more money spent on spam production, which means more spam added to the backlog, which means a larger message backlog. We just completed the wealth of information feedback loop, which encourages the supply of spam. When the time required to complete a task is greater than the time endowment, information overload occurs (Simon, 1971; Schultz and Vandenbosch, 1998). As information overload increases, more messages are deleted (Fallows, 2003). This completes the balancing feedback loop called the poverty of attention.

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The feedback structure in Figure 1 is an extension of a generic structure called limits-to-growth (Senge, 1990). To understand the dynamics of a system depicted in Figure 1, we build a corresponding computational model, which we then use to perform numerical experiments. A detailed description of the model is given in the Appendix. In the following sections we describe the experiments.

spam as a fraction of all email traff ic

+ spam production +

+

perc ent of false-negatives fraction of valuable spam messages

spam funding +

R revenue

wealth of inform ation

+

spam added to bac klog -

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message bac klog

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av. fraction of valuable spam messages in bac klog + + valuable spam messages proc essed

volume of regular messages

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B + poverty of attention

information overload

the daily time required to + proc ess messages daily time endowment

Figure 1: Causal relationships in the spam system

Baseline Behavior Our first experiment emulates the growth of spam. Figure 2 presents resulting trajectories of selected variables. The dynamics can be understood in terms of the causal links of the diagram in Figure 1. We start the simulation at low levels of spam funding


(line 2 in Figure 2) and correspondingly, spam production (line 3) – they are set to near zero levels. Message backlog (line 4) is also relatively low. Email recipients have enough time to process messages and hence experience relatively low levels of information load (line 5) due to email. Correspondingly, hardly any email is deleted (line 6) before being processed. Gradually, the revenue from spam encourages more spam production (see causal logic in Figure 1). Some of the messages that are processed are valuable to the recipients (line 1). For a while, as more spam is sent, the number of valuable spam messages processed is also increasing. If attention of recipients were not limited, the reinforcing feedback loop, marked with an R in Figure 1, would result in an exponential growth of spam production. However, spam does not increase indefinitely. Around time 80, information overload (line 5) starts to increase rapidly due to the swelling message backlog; this leads to more email being deleted (line 6). Because the forces of the balancing loop, labeled as poverty of attention in Figure 1, become significant towards the end of the simulation, the growth of variables eventually stagnates leading to the distinctive S-shaped trajectories (Figure 2).

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1 1 1 1 1 1 1 1 1 1 valuable spam messages processed messages/Day 2 2 2 2 2 2 2 2 2 2 2 2 2 spam funding $ 3 3 3 3 3 3 3 3 3 3 3 spam production messages/Day 4 4 4 4 4 4 4 4 4 4 4 4 message backlog messages 5 5 5 5 5 5 5 5 5 5 5 5 informaiton overload Dmnl 6 6 6 6 6 6 6 6 6 6 6 6 deletion messages/Day

Figure 2: Transitory and steady state behavior of an email marketing system

The convergence of the system to a steady state may seem unreasonable in light of the global growth of spam volume. But Melville et al. (2006) find that the spam flow to an educational institution, which has a fixed number of email inboxes, is rather steady during an academic year. Our model also assumes a fixed number of recipients, and therefore a steady state appears to be a reasonable outcome. Steady state values for stocks can be found in the Appendix. Unintended Consequences of Filtering Research on email marketing and spam has focused on the development of evermore effective spam filters. In theory, such mechanisms work well at mitigating the information overload by accurately detecting a message with text that indicates that it is


likely to be unwanted. There is thus a logic embedded in the decision to deploy filters as a solution, and to the expectation that better filters will lead to less spam. However, despite apparent improvement in filtering techniques, the global volume of spam only appears to be growing (Garretson, 2007). As we demonstrate in this section, a view of email as a system suggests that improvements to filtering software may contribute to the growth of the total flow of spam. We allow for the filtering technology to improve, which we simulate by gradually lowering the percent of false-negatives and the percent of false-positives beginning at time 30. To avoid the transitory dynamics, we start the simulation in the steady state. Trajectories from the simulation are presented in Figure 3; the causal logic of the simulation can be followed with the help of Figure 1. The percent of false-negatives is line 1 in Figure 3. Note that we omit the false-positives trajectory from Figure 3 because it is very similar to the trajectory of false-negatives. After day 30, the filter is becoming better at identifying unwanted spam messages (line 1 is dropping), and therefore the average fraction of valuable spam messages (line 2) is improving. This leads to more valuable spam messages being processed (see Figure 1), more sponsor revenue (line 3), more spam funding (see Figure 1), and more spam production (line 4 in Figure 3). During the simulation, we keep track of the measure called spam as a fraction of all email traffic (see Figure 1 and Appendix for exact definition). Given that volume of regular messages is constant, as spam production increases, spam as fraction of all email traffic also increases (line 6). The growth of the spam production ensures that the spam added to backlog (line 5) returns to its steady state level.

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In summary, spam production is higher in the presence of better filtering, which may be viewed as an unintended consequence of filtering. filter 0.245 0.08 430,000 30,000 3,240 1

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1 1 1 1 1 1 1 1 1 1 1 Dmnl percent of false negatives 1 2 2 2 2 2 2 2 2 av fraction of valuable spam messages in backlog 2 Dmnl 3 3 3 3 3 3 3 3 3 3 3 3 sponsor revenue $/Day 4 4 4 4 4 4 4 4 4 4 4 spam production messages/Day 5 5 5 5 5 5 5 5 5 5 spam added to backlog messages/Day 6 6 6 6 6 6 6 6 6 6 6 spam as fraction of all email traffic Dmnl

Figure 3: A simulation of an improved filter

The targeting effect of filters The outcome that filtering contributes to the growth of spam may be puzzling at first. But this result can be better understood if one realizes that filtering mitigates the problem of asymmetric information. An information asymmetry occurs when one party participating in an exchange knows something that the other party does not (Varian, 1992). A classical example of a situation with information asymmetries is a used car market (Akerlof, 1970; Jensen, 2007). In the case of email, the information asymmetry is due to the fact that spammers do not know the preferences of recipients.


Targeting improves response rates of a marketing campaign (Chittenden and Rettie, 2003; Ansari and Mela, 2003). Message targeting involves choosing message content, format, and timing so that it matches the particular needs and preferences of the recipient to whom it is addressed. Spammers tend not to use targeting, which is not only because targeting is costly, but also because spammers – more so than legitimate email marketers – have a limited ability to obtain information needed to customize their messages to the preferences of each recipient. Instead, they rely on mass mailings rather than customized messages. We now examine the impact of targeting. To do so, we increase the fraction of valuable spam messages (Figure 1). After the fraction of valuable spam messages is increased on day 30, the average fraction of valuable spam messages in backlog also rises (line 1 in Figure 4). Correspondingly, valuable spam messages processed (line 2), sponsor revenue (line 3), and spam production (line 4) grow. As is clear from Figure 1, filtering and targeting have very similar effects on spam production, which is confirmed by our experiments. In the cases of filtering and targeting, spam production increases. By deploying filters, inbox owners inadvertently enable the same outcome as if the asymmetry of information is reduced and spammers are able to target their messages.

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Figure 4: A simulation of improved targeting

4. Summary and Recommendations Despite the lucrative nature of email marketing, consumers are overloaded with information, including both wanted and unwanted communication. However, the overall result of mitigation strategies addressing individual dimensions of the problem has led to “record growth, and record irritation,” with spam accounting for 88 percent of all e-mail traffic in August of 2007 (Garretson, 2007). The fundamental thesis of this article is that the email marketing infrastructure is a complex system requiring analysis that takes account of the whole system. A system dynamics model that captures the relationships in


the complex system is shown to identify underlying dynamics and reveal unexpected consequences. Thus far, the primary industry response to excessive levels of spam has been to deploy filters. As the model developed herein explains, this has given spammers the equivalent of a useful information tool equivalent to targeting their messages. Consequently, it is likely that filters encourage spam production. A number of other approaches to mitigating information overload have been proposed, including variants of email postage, attention bonds, and reverse Turing tests. Before industry and regulators choose any of these approaches for general implementation, it would be beneficial to turn to system dynamics for an analysis of the likely outcomes. Unlike filtering, some of these proposals would change the nature of email as a free, anonymous communications medium. The experience with filtering suggests that before taking such drastic measures to stem the flow of spam, there should be confidence that the resulting system has been thoroughly evaluated, and that the behavioral and informational consequences of the proposed changes have been analyzed. Such an analysis must take into account the dynamic feedback and causal-loop mechanisms that govern the system. The model presented and analyzed in this paper exemplifies the approach, and demonstrates the benefits of system dynamics modeling to analyze the email-marketing industry.

Appendix: System Dynamics Model Here we present a mathematical formulation of the model, which enables complete reproduction and simulation of the model. Two types of broadcasters are modeled. One broadcaster sends only regular email; each regular message is valuable.

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The other broadcaster sends spam, which is financed by sponsors. Only a fraction of spam is valuable. The model has a coflow formulation (for definition, see Sterman, 2000) in the Recipients sector, which allows keep track of the message backlog, the number of valuable messages (both regular email and spam) in the backlog, the number of valuable spam messages in the backlog, and the number of spam messages (valuable and nonvaluable) in the backlog. The technology firm supplies a filter. The filter may block a valuable message, which is referred to as a false-positive error. It may allow a non-valuable spam message through – a false-negative error. The Receiver Operating Characteristic (ROC) curve (Cormack, 2006) summarizes the tradeoff between the two errors (Figure 5). The area above the ROC curve is the aggregate misspecification rate, which we denote as Φ . If we assume that the tradeoff between false-positives and false-negatives remains constant, then when the aggregate misspecification rate changes, we can calculate the values for false-positives and false-negatives given some initial values. The best filter corresponds to the upper left corner of the graph. The worst filter is the lower right corner of the graph. A no filter situation is presented by the lower left corner of the graph.

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η1 1

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The model has been implemented and simulated in Vensim DSS. Below, parameters and variables are organized according to the four sectors of the model: recipients, sponsors, broadcasters and a technology firm.

Recipients Parameters

R

number of recipients

100 recipients

aR

average volume of regular communication

10 messages/day/recipient

TR

20 minutes/day/recipient

γ τ

time a typical recipient spends on email normal response delay

4 days

message processing time

1 minute/message

ρ

fraction of valuable spam messages

0.01 dimensionless

d

average sensitivity to overload

3 dimensionless

Variables

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message backlog

(d / dt ) M = mi − mo − md

volume of regular messages

a = aR R

messages that are identified as valuable are added to the backlog

mi = bi + ai

spam added to backlog

bi = (1 − η1 ) ρ b + η 2 (1 − ρ ) b

regular email added to backlog

ai = (1 − η1 ) a

daily time endowment

T = RTR

the daily time required to process messages

T * = ( M γ )τ

information overload

λ = T* T

processed messages

mo = min (T τ , M γ )

deletion

md = eod ⋅ d ⋅ ( M γ )

valuable messages in the backlog

dV AB = viAB − voAB dt

valuable messages added to the backlog

viAB = (1 − η1 )( a + ρ b )

valuable messages that are either processed or deleted

voAB = ( mo + md ) β

average fraction of valuable messages of all types (spam and regular email) in the backlog

β=

valuable spam messages in the backlog

dV B = viB − voB dt

valuable spam messages added to the backlog

viB = (1 − η1 ) ρ b

valuable spam messages that are either processed or deleted

voB = ( mo + md ) ρ

average fraction of valuable spam messages in the backlog

ρ=

spam messages in the backlog

dB = bi − bo dt

removal of spam messages from the backlog either through processing or deletion

bo = ( mo + md ) θ

V AB M

VB M

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average fraction of spam messages (valuable and nonvaluable) in the backlog

θ =

spam as fraction of all email traffic

b ( a + b)

B M

Sponsors Parameters

τS

spam budget duration

10 day

ξ

fraction of surplus allocated to spam funding

0.1 dimensionless

r

marginal revenue per spam message

2000 $/message

Variables spam funding

(d

dt ) S = i − e

expenditure on spam by sponsors

e = S τS

valuable spam messages processed

bp = mo ρ

sponsor revenue

RS = rbp

sponsor cost

CS = e

sponsor surplus

π = RS − CS

spam budget allocation

i = max (ξπ , 0 )

Broadcasters Parameter

pb

price of sending a spam message

1 $/message

Variable spam production

b = e pb

Technology firm Parameters

η1

initial percent of falsepositives

0.01 dimensionless

η2

initial percent of falsenegatives

0.12 dimensionless

Φ*

tolerable misspecification rate

0.1 dimensionless

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Φ

original misspecification rate

0.1 dimensionless

λφ

filter improvement delay

10 day

τφ

filter shock delay

1 day

ε

negative shock to filter

0 dimensionless

Variables misspecification rate

(d

filter degradation

φi = ε τ φ

filter improvement

φo = ( Φ − Φ* ) λφ

percent of false-positives

η1 = αη1 , α = Φ Φ

percent of false-negatives

η2 = αη2

Lookup function: effect of overload on deletion, eod

Steady state values for variables

M

Message backlog

8830 messages

dt ) Φ = φi − φo


V AB

valuable messages in backlog

2920 messages

VB

valuable spam messages in backlog

492 messages

B S Φ

spam messages in backlog

6402 messages

communication funding

202796 $

aggregate misspecification rate

0.1 dimensionless

References Akerlof G A (1970). The market for "lemons": Quality uncertainty and the market mechanism. Quarterly Journal of Economics 84: 488-500. Androutsopoulos I, Magirou E F and Vassilakis D K (2005). A game theoretic model of spam e-mailing. In. Second Conference on Email and Anti-Spam CEAS 2005. July 2122, 2005, Mountain View, California. Ansari A and Mela C F (2003). E-customization. Journal of Marketing Research 40 (2): 131-146. basu a k, basu a and batra r (1995). Modeling the response pattern to direct marketing campaigns. Journal of Marketing Research 32 (2): 204-212. Chittenden L and Rettie R (2003). An evaluation of email marketing and factors affecting response. Journal of Targeting, Measurement and Analysis for Marketing 11 (3): 203 – 218. Cormack G V (2006). The trec 2005 spam filter evaluation track. Virus Bulletin (January): S2. CROSON D, D’ARCY J, SCHUFF D and TURETKEN O (2005). Managing e-mail overload: Clustering to increase attention supply. http://www.sims.berkeley.edu/~hal/Papers/2004/reliability. Dai R and Li K (2004). Shall we stop all unsolicited email messages? In. First Conference on Email and Anti-Spam CEAS 2005. July 30-31, 2004, Mountain View, California. Elkin T (2007). Jefferies: Online ad spending could exceed $60b by 2010. Online Media Daily. Fallows D (2003). Spam: How it is hurting email and degrading life on the internet.

25


Fawcett T (2003). "in vivo" spam filtering: A challenge problem for data mining. KDD Explorations 5 (2). Garretson C (2007). The summer of spam: Record growth, record irritation. Network World. Goldman E (2006). A coasean analysis of marketing. Wisconsin Law Review 2006 (4). Goodman J, heckerman and Rounthwaite (2005). Stopping spam. Scientific American 292 (4): 42-45. Gratton E (2004). Dealing with unsolicited commercial emails: A global perspective. journal of internet law 7 (12): 3-13. Gray A and Haahr M (2004). Personalised, collaborative spam filtering. JENSEN C (2007). Their titles laundered, the cars are still lemons. The New York Times Online (August 26). Kofman F and Senge P (1993). Communities of commitment: The heart of learning organizations. Organizational Dynamics: 5-23. Loder T, Van Alstyne M and Wash R (2006). An economic response to unsolicited communication. Advances in Economic Analysis and Policy 6 (1): 1-38. McCloskey W (2006). 2006 retail white paper. Email Data Source, New York: 1-15. McWilliams B (2005). Spam kings. O'Reilly: Sebastopol, CA. Melville N, Stevens A, Plice R K and Pavlov O V (2006). Unsolicited commercial email: Empirical analysis of a digital commons. International Journal of Electronic Commerce 10 (4): 143-168. moxnes (1998). Not only the tragedy of the commons: Misperceptions of bioeconmics. management science 44 (9): 1234-1248. Nussey B (2007). Spam is on the rise again. silverPOP. Pavlov O V, Melville N and Plice R (2005). Mitigating the tragedy of the digital commons: The problem of unsolicited commercial e-mail. Communications of the Association for Information Systems 16: 73-90. Randers J (1980). Guidelines for model conceptualization. In: Randers J(ed. Elements of the system dynamics method, Vol. Productivity Press: Cambridge MA, pp. 117-138.

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Rust R T and Espinoza F (2006). How technology advances influence business research and marketing strategy. Journal of Business Research 59: 1072-1078. Schultz U and Vandenbosch B (1998). Information overload in a groupware environment: Now you see it, now you don't. Journal of Organizational Computing & Electronic Commerce 8 (2): 127-151. Schwartz B (2004). The paradox of choice: Why more is less. HarperCollins: New York, NY. Senge P M (1990). The fifth discipline: The art and practice of the learning organization. Doubleday/Currency: New York. simon h (1971). Designing organizations for an information-rich world. In: Greenberger M(ed. Computers, communications, and the public interest, Vol. The Johns Hopkins Press: Baltimore, MD, pp. 38-52. Sterman J D (2000). Business dynamics. McGraw-Hill: Boston, MA. Varian H (1992). Microeconomic analysis. W. W. Norton & Company: New York. Woodside A (2006). Advancing systems thinking and building microworlds in business and industrial marketing. Journal of Business and Industrial Management 21 (1): 24-29.

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