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Quantitative Marketing and Economics

Erschienen in:

01.09.2007

(Noisy) communication

verfasst von: Bharat N. Anand, Ron Shachar

Erschienen in: Quantitative Marketing and Economics | Ausgabe 3/2007

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Abstract

Communication is central to many settings in marketing and economics. A focal attribute of communication is miscommunication. We model this key characteristic as a noise in the messages communicated, so that the sender of a message is uncertain about its perception by the receiver, and then identify the strategic consequences of miscommunication. We study a model where competing senders (of different types) can invest in improving the precision of the informative but noisy message they send to a receiver, and find that there exists a separating equilibrium where senders’ types are completely revealed. Thus, although communication is noisy it delivers perfect results in equilibrium. This result stems from the fact that a sender’s willingness to invest in improving the precision of their messages can itself serve as a signal. Interestingly, the content of the messages is ignored by the receiver in such a signaling equilibrium, but plays a central role by shaping her beliefs off the equilibrium path (and thus, enables separation between the types). This result also illustrates the uniqueness of the signaling model presented here. Unlike other signaling models, the suggested model does not require that the costs and benefits of the senders will be correlated with their types to achieve separation. The model’s results have implications for various marketing communication tools such as advertising and sales forces.

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See the following articles in the New York Times: “Bush, Facing Criticism, Abandons Debate Stance” (September 9, 2000); “One Debate Down, Three to Go” (September 10, 2000); “Dropping All of His Objections, Bush Agrees to Panel’s Debates” (September 15, 2000); and, “Candidates Agree on Formats for Three Debates” (September 17, 2000).

For example, in their text book on advertising Belch and Belch (2007) write: “Throughout the communication process, the message is subject to extraneous factors that can distort or interfere with its reception. This unplanned distortion or interference is known as noise.”

Put differently, in any signaling model there is more than one sender’s type. However, in a standard signaling model the number of senders is limited to one. Thus, these models do not accommodate the possibility that there is more than one player whose actions are observed by the receiver.

While the model here explores communication in general, Anand and Shachar (2006) study the specific application of advertising. As a result, that paper focuses on a quite different mechanism of information transmission. Specifically, while the study here, message precision serves as the signal, in the other study the signaling is done via media selection (i.e., targeting).

Bhardwaj et al. (2005) show that a monopolist who offers a high quality product prefers to give up control over the message and allow the buyer to ask whatever she wants about the product. The revelation format facilitates the use of price as a signaling mechanism.

An important strand of this literature, going back to Crawford and Sobel (1982), examines how information is strategically transmitted through messages in cheap talk games.

For example, the information systems theory of communication (Shannon 1948) and subsequent papers in economics that draw on it, notably the theory of teams (Marshack and Radner 1972). In all those studies agents are non-strategic.

The setup of our model is different in other aspects such as the cost of sending a mes sage. Exceptions are those that focus on deception in which the cost of a non-truthful messsage is higher than the cost of a truthful one; see, for example, Sanchirico (2001) and Deneckere and Severinov (2003).

Exceptions are Hertzendorf and Overgaard (2001) and Daughety and Reinganum (2000).

In other words, by ignoring the cases that both senders are of the same type (either L or H), we focus on the interesting scenario in which there is some difference between the two senders and thus one of them is better than the other from the receiver’s point of view.

(a) Of course, when the message is so simple, the probability of miscommunication is minuscule. However, in reality messages are rarely so simple.

(b) Like other signaling models, L can try and deceive the receiver by imitating H’s actions. However, he is not allowed to deceive in the message content. Still, as pointed out, the perception of the message is random and thus there is still a chance that the receiver will interpret a message from L as saying “I am H.”

The last statement is true when at least one player sends a message. When neither one of them sends a message the probability of being selected is, obviously, \(\frac{1}{2}\) for each of the senders.

Recall that a high q implies that the content of the message is more likely to indicate that this player is L.

(a) Another way to think about the limits of the interval for c is the following. They are based on the probability that L is selected when any sender deviate from his equilibrium strategy (which is 0.5 if H deviates and 1 − q if L deviates). This reflects the fact that both senders in this game are competing over the “market share” of L.

(b) This interval is at a higher cost level than the interval for the non-strategic equilibrium. This is not surprising: in the non-strategic equilibrium the cost should be low enough for H to send a message. In the separating equilibrium, it should be high enough to deter L from mimicking.

See http://www.vanquish.com/press/ps_clean_email.shtml.

For example, in many situations ad agencies find it difficult to increase the precision of their ads, because they need to ensure that the ad is memorable, attractive, etc. As the ad become more precise, essential aspects of memorability and attractiveness are likely to be blemished.

Notice, for example, that if C(q) = c(q − 0.5)², then C ^′(0.5) = 0 for any cost parameter.

Recall that q° is the precision selected by H in the non-strategic equilibrium and that it satisfies the following equation C ^′(q°) = 1.

Notice that the suggested explanation differs from the signaling theory of advertising presented by Kihlstrom and Riordan (1984) and Milgrom and Roberts (1986) in various significant ways. For example, while the standard theory assumes that the content of the ads is empty, the suggested model focuses on informative advertising. Furthermore, while in the standard model the separation is enabled by repeated purchases, here we do not require repeated purchases and the separation is based on the usefulness of ads’ content off the equilibrium path.

Interestingly, Zhao (2000) shows that under a certain condition the result of the studies mentioned above is reversed and higher advertising is associated with a lower quality firm. The condition is that advertising spending does not serve only as a signal but also as a determinant of the size of the market (via raising awareness). Furthermore, Desai (2000) demonstrates that advertising spending can serve as a signal of quality to the retailer.

See discussion in Bhardwaj et al. (2005).

For example, according to Forrester Research, 26% of online retailers currently allow individuals to input product reviews on the firm’s website. Until recently, many firms reviewed these reviews and rejected those that were negative about the firm’s product. Some firms are now changing their policy. For example, web retailer Overstock.com “had been relying on its merchandising group—the employees responsible for deciding which products to sell on the site—to monitor reviews submitted by customers, but found that the group tended to approve only positive reviews. In January, the Salt Lake City-based company changed the monitoring responsibilities to its marketing team. The company now says it posts both positive and negative comments. “We learned that customers won’t trust the site if there are only positive reviews,” says Tad Martin, senior vice president of merchandising and operations at Overstock.’ ” (“Giving Reviews the Thumbs Down”, Wall Street Journal, August 4, 2005).

We thank Dina Mayzlin for this example.

It is well known that political candidates occasionally increase the nosiness of their messages in order to maximize their winning probability. The rationale behind such a strategy is related to the “median voter theory” which suggests minimum differentiation between the candidates.

If q ₂ < q ₁, the receiver bases her decision only on m ₁ (i.e., the perceived content of the more precise message). The probability that m ₁ = − 1 (i.e., indicating that 2 is H) is 1 − q ₁. Thus, E(d ₂|q ₂,q ₁) = 1 − q ₁.

If q ₂ = q ₁, the probability that m ₁ = − 1 and m ₂ = 1 (i.e., both messages indicate that 2 is H and the receiver selects 2) is equal to \( (1-q_{1})^{2}\) and the probability that the messages contradict one another (in this case the receiver selects one of the senders randomly) is 2q ₁(1 − q ₁). Thus, \(E(d_{2}|q_{2},q_{1})=(1-q_{1})^{2}+\frac{1}{2} 2q_{1}(1-q_{1})=(1-q_{1})\).

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Titel: (Noisy) communication
verfasst von: Bharat N. Anand
Ron Shachar
Publikationsdatum: 01.09.2007
Verlag: Springer US
Erschienen in: Quantitative Marketing and Economics / Ausgabe 3/2007
Print ISSN: 1570-7156
Elektronische ISSN: 1573-711X
DOI: https://doi.org/10.1007/s11129-007-9028-2