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Defensive, Offensive, and Generic Advertising in a Lanchester Model with Market Growth

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Abstract

The paper considers a duopolistic market in which firms compete over time through their respective advertising efforts. In contrast to earlier work in advertising competition, the paper supposes that each firm may choose among three types of advertising: offensive advertising which has the purpose of attracting customers from the rival firm, defensive advertising which aims at protecting a firm’s customer base from the competitors’ attacks, and generic advertising which aims at enhancing industry sales. We address questions like: How should an advertising strategy, for each of the three types of advertising effort, be designed? How would the corresponding time paths of sales look like? The paper uses differential game theory to answer these questions and provides closed-form analytical expressions for equilibrium advertising strategies and sales rate paths. It is found that advertising strategies can be expressed in terms of the shadow prices of the firms’ sales rates and the model parameters. Two combinations of theses advertising are optimal: all three advertising together and both offensive and defensive advertising. As to the latter, an essential assumption is that offensive advertising is more cost-effective than defensive advertising.

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Notes

  1. One example is the telecommunications industry where customer loyalty is relatively low and the cost of attracting a new customer can be high.

  2. A recent example of the use of generic advertising is the European beer market which has been shrinking in recent years. The trade association “Brewers of Europe” plans a campaign in 2014 to stimulate beer sales in general. A campaign, named “Let There Be Beer,” ran in 2013 in the UK.

  3. Market contraction has been studied, although in a rather stylized fashion in, e.g., Wang and Wu [14], Espinosa and Mariel [5]. Market contraction is disregarded in the current research.

  4. For the reason that defensive advertising is absent in their model, Bass et al. [1, 2] are able to find analytical solutions for an asymmetric case.

  5. Martín-Herrán et al. [10] also use a linear formulation of attraction rates. Moreover, they include multiplicative interaction between offensive and defensive advertising.

  6. In Bass et al. [1, 2], offensive and generic advertisings have the same cost function, that is, \(c_{a}=c_{g}.\)

  7. A common reason why products and services have a finite lifetime is the emergence of technological innovations that generate more attractive substitutes.

  8. This result can also be found in Sorger [12] and Bass et al. [1, 2].

  9. An explicit solution for \(\tilde{T}\) as a real number does not exist\(.\)

References

  1. Bass FM, Krishnamoorthy A, Prasad A, Sethi SP (2005a) Generic and brand advertising strategies in a dynamic duopoly. Mark Sci 24(4):556–568

    Article  Google Scholar 

  2. Bass FM, Krishnamoorthy A, Prasad A, Sethi SP (2005b) Advertising competition with market expansion for finite horizon Firms. J Ind Manag Optim 1(1):1–19

    Article  MATH  MathSciNet  Google Scholar 

  3. Dearden JA, Lilien GL (2001) Advertising coopetition: Who pays? Who gains? In: Baye MR, Nelson JP (eds) Advances in applied microeconomics, vol 10. JAI Press, Amsterdam, pp 203–219

    Google Scholar 

  4. Erickson GM (1993) Offensive and defensive marketing: closed-loop duopoly strategies. Mark Lett 4: 285–295

    Article  Google Scholar 

  5. Espinosa MP, Mariel P (2001) A model of optimal advertising expenditures in a dynamic duopoly. Atl Econ J 29:135–161

    Article  Google Scholar 

  6. Fruchter GE (1999) Oligopoly advertising strategies with market expansion. Optim Control Appl Methods 20:199–211

    Article  MathSciNet  Google Scholar 

  7. Huang J, Leng M, Liang L (2012) Recent developments in dynamic advertising research. Eur J Oper Res 220(3):591–609

    Article  MATH  MathSciNet  Google Scholar 

  8. Jørgensen S, Zaccour G (2004) Differential games in marketing. Kluwer Academic Publishers, Boston

    Book  Google Scholar 

  9. Krishnamoorthy A, Prasad A, Sethi SP (2010) Optimal pricing and advertising in a durable-goods duopoly. Eur J Oper Res 200:486–497

    Article  MATH  Google Scholar 

  10. Martín-Herrán G, McQuitty S, Sigué SP (2012) Offensive versus defensive marketing: What is the optimal spending allocation? Int J Res Mark 29:210–219

    Article  Google Scholar 

  11. Piga C (1998) A dynamic model of advertising and product differentiation. Rev Ind Org 13:509–522

    Article  Google Scholar 

  12. Sorger G (1989) Competitive dynamic advertising: a modification of the case game. J Econ Dyn Control 13:55–80

    Article  MATH  MathSciNet  Google Scholar 

  13. Van Long N, Léonard D (1992) Optimal control theory and static optimization in economics. Cambridge University Press, Cambridge

    Google Scholar 

  14. Wang Q, Wu A (2001) A duopolistic model of dynamic competitive advertising. Eur J Oper Res 128: 213–226

    Article  MATH  Google Scholar 

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Correspondence to Simon-Pierre Sigué.

Appendix: Proofs

Appendix: Proofs

Proof of Proposition 1

It has already been proved in Lemma 1 that \( \varphi (t)>\psi (t)\) for all \(t\) and in both cases. In Case 1, use () to show that \(\dot{\psi }(t)<0\) for all \(t\in \left[ 0,T\right] . \) Since \(\psi (T)=0\) must hold, it follows that \(\psi (t)>0\). The result \( \varphi (t)>0\) follows directly from (13). Positivity of both shadow prices leads to \(\varphi (t)+\psi (t)>0\) which implies a positive generic advertising rate. In Case 2, we use (11) to show that \( \dot{\psi }(t)>0\) for all \(t.\) Since \(\psi (T)=0,\) it follows that \(\psi (t)<0 \) for all \(t.\) To prove the remaining results, we need the sign of

$$\begin{aligned} \varphi (t)+\psi (t)&=\frac{2mC_{2}\left( T-t\right) }{C_{2}-C_{1}} \\&\quad -\frac{\left( C_{1}+C_{2}\right) \sqrt{m\left( C_{2}-C_{1}\right) }\tanh \sqrt{m\left( C_{2}-C_{1}\right) }\left( T-t\right) }{\left( C_{1}-C_{2}\right) ^{2}} \end{aligned}$$

which, introducing three constants

$$\begin{aligned} B_{1}\triangleq \frac{2mC_{2}}{C_{2}-C_{1}}<0,\text { }\gamma \triangleq \sqrt{m\left( C_{2}-C_{1}\right) }>0,\text { }B_{2}\triangleq \frac{\left( C_{1}+C_{2}\right) \gamma }{\left( C_{1}-C_{2}\right) ^{2}}<0 \end{aligned}$$

and defining \(h(t)=\varphi (t)+\psi (t),\) can be written more conveniently as

$$\begin{aligned} h(t)=B_{1}\left( T-t\right) -B_{2}\tanh \gamma \left( T-t\right) . \end{aligned}$$

Next we determine the essential properties of function \(h.\) It holds that \( h(T)=0\) and

$$\begin{aligned} h(0)\left( {\begin{array}{c}>\\ <\end{array}}\right) 0\Leftrightarrow \frac{2C_{2}}{C_{1}+C_{2}}\left( {\begin{array}{c}<\\ >\end{array}}\right) \frac{ \tanh \gamma T}{\gamma T} \end{aligned}$$

where \(2C_{2}/\left( C_{1}+C_{2}\right) \in \left( 0,1\right) .\) The function \(z\left( T\right) =\tanh \gamma T/(\gamma T)\) is decreasing and \( z\left( T\right) \in \left( 0,1\right) .\) We conclude that there exists a value of \(T,\) say \(\tilde{T},\) defined by \(2C_{2}/\left( C_{1}+C_{2}\right) =\tanh \gamma \tilde{T}/\left( \gamma \tilde{T}\right) \) such that \(h\left( 0\right) >0\) if \(T<\tilde{T}\) and \(h\left( 0\right) \le 0\) if \(T\ge \tilde{ T}\).Footnote 9 Function \(h\) is strictly concave since

$$\begin{aligned} \ddot{h}(t)=-2B_{2}\gamma ^{2}\left( \tanh \gamma \left( T-t\right) \right) \left( \tanh ^{2}\gamma \left( T-t\right) -1\right) <0\text { for }t\in [0,T). \end{aligned}$$

Using the properties derived for function \(h(t)\) shows that if \(T<\tilde{T},\) then \(h(t)\) is positive for \(t\in [0,T).\)If \(T>\tilde{T},\) then \(h(t)\) is negative on an initial interval \([0,t^{*})\) and positive on a terminal interval \((t^{*},T).\) Q.E.D.

Proof of Proposition 3

We solve the inhomogeneous equations that apply during time intervals \(\left[ 0,T\right] \) and \([t^{*},T],\) respectively\(.\) The differential equations for \(S_{1}\left( t\right) \) and \( S_{2}\left( t\right) \) can be transformed into a second-order equation for \( S_{1}\left( t\right) \):

$$\begin{aligned} \ddot{S}_{1}\left( t\right) +\left( 2p(t)-\frac{\dot{p}(t)}{p(t)}\right) \dot{S}_{1}\left( t\right) =\dot{q}(t)+2p(t)q(t)-\dot{p}(t)\frac{q(t)}{p(t)} \\ \Leftrightarrow \ddot{S}_{1}\left( t\right) +f(t)\dot{S}_{1}\left( t\right) =g(t) \end{aligned}$$

which admits the solution

$$\begin{aligned} S_{1}\left( t\right) =\kappa _{1}+\kappa _{2}\int \hbox {e}^{-F(t)}\hbox {d}t+\int \hbox {e}^{-F(t)}\left( \int \hbox {e}^{F(t)}g(t)\hbox {d}t\right) \hbox {d}t \end{aligned}$$
(25)

where \(F(t)=\int f(t)\hbox {d}t\) and \(\kappa _{1},\kappa _{2}\) are constants of integration. First we determine the integral \(F:\)

$$\begin{aligned} F&=\int \left( 2p(t)-\frac{\dot{p}(t)}{p(t)}\right) \hbox {d}t=\int \left( \frac{ 4\gamma }{3}\tanh \gamma \left( T-t\right) -\frac{\gamma \left( \tanh ^{2}\gamma \left( T-t\right) -1\right) }{\tanh \gamma \left( T-t\right) } \right) \hbox {d}t \\&=-\ln \left( \hbox {e}^{2\gamma \left( t-T\right) }-1\right) +\ln \left( \hbox {e}^{2\gamma \left( t-T\right) }+1\right) -\frac{4}{3}\ln \left( \hbox {e}^{2\gamma \left( t-T\right) }+1\right) -\frac{4\gamma }{3}\left( 2T-t\right) \end{aligned}$$

which leads to

$$\begin{aligned} \hbox {e}^{F}=\hbox {e}^{\frac{4\gamma }{3}\left( t-2T\right) }\frac{\left( \hbox {e}^{-2\gamma \left( T-t\right) }+1\right) ^{-\frac{1}{3}}}{\hbox {e}^{-2\gamma \left( T-t\right) }-1};\quad \hbox {e}^{-F}=\hbox {e}^{-\frac{4\gamma }{3}\left( t-2T\right) }\frac{ \hbox {e}^{-2\gamma \left( T-t\right) }-1}{\left( \hbox {e}^{-2\gamma \left( T-t\right) }+1\right) ^{-\frac{1}{3}}}. \end{aligned}$$

Hence

$$\begin{aligned} \kappa _{2}\int \hbox {e}^{-F(t)}\hbox {d}t=\kappa _{2}\frac{3\hbox {e}^{\frac{4\gamma }{3}\left( 2T-t\right) }\left( 1+\hbox {e}^{-2\gamma \left( T-t\right) }\right) ^{\frac{4}{3}}}{ 4\gamma }. \end{aligned}$$
(26)

The next task is to determine the integral \(\int \hbox {e}^{-F(t)}\left( \int \hbox {e}^{F(t)}g(t)\hbox {d}t\right) \hbox {d}t.\) Here we have

$$\begin{aligned} \int \hbox {e}^{F(t)}g(t)\hbox {d}t= & {} \frac{\theta \left( T-t\right) \left( 1+\hbox {e}^{-2\gamma \left( T-t\right) }\right) +\gamma \eta \left( 1-\hbox {e}^{-2\gamma \left( T-t\right) }\right) }{\hbox {e}^{-\frac{4\gamma }{3}\left( t-2T\right) }\left( \hbox {e}^{4\gamma \left( t-T\right) }-1\right) \left( \hbox {e}^{-2\gamma \left( T-t\right) }+1\right) ^{\frac{1}{3}}},\\ \hbox {e}^{-F(t)}\int \hbox {e}^{F(t)}g(t)\hbox {d}t= & {} \frac{\theta \left( T-t\right) \left( 1+)\hbox {e}^{-2\gamma \left( T-t\right) }\right) +\gamma \eta \left( 1-\hbox {e}^{-2\gamma \left( T-t\right) }\right) }{\hbox {e}^{-2\gamma \left( T-t\right) }+1} \end{aligned}$$

and then

$$\begin{aligned} \int \hbox {e}^{-F(t)}\left( \int \hbox {e}^{F(t)}g(t)\hbox {d}t\right) \hbox {d}t=-2T\gamma \eta +\left( T\theta +\gamma \eta \right) t-\frac{\theta t^{2}}{2}-\eta \ln \left( 1+\hbox {e}^{2\gamma \left( t-T\right) }\right) . \end{aligned}$$
(27)

Using (25), (26), and (27) yields the equilibrium sales rate trajectories:

$$\begin{aligned} S_{1}\left( t\right)= & {} \kappa _{1}+\kappa _{2}\frac{3\hbox {e}^{\frac{4\gamma }{3} \left( 2T-t\right) }\left( 1+\hbox {e}^{-2\gamma \left( T-t\right) }\right) ^{\frac{4 }{3}}}{4\gamma } \\&\quad -2T\gamma \eta +\left( T\theta +\gamma \eta \right) t-\frac{\theta }{2} t^{2}-\eta \ln \left( 1+\hbox {e}^{2\gamma \left( t-T\right) }\right) \nonumber \\ S_{2}(t)= & {} \kappa _{1}-\kappa _{2}\frac{3\hbox {e}^{\frac{4\gamma }{3}\left( 2T-t\right) }\left( 1+\hbox {e}^{2\gamma \left( t-T\right) }\right) ^{\frac{4}{3}}}{ 4\gamma } \nonumber \\&\quad -2T\gamma \eta +\left( T\theta +\gamma \eta \right) t-\frac{\theta }{2} t^{2}-\eta \ln \left( 1+\hbox {e}^{2\gamma \left( t-T\right) }\right) . \nonumber \end{aligned}$$
(28)

Setting \(\theta =\eta =0\) provides the solutions of the homogeneous equations on the interval \(\left[ 0,t^{*}\right] \):

$$\begin{aligned} S_{1}\left( t\right)= & {} \kappa _{1}+\kappa _{2}\int \hbox {e}^{-F(t)}\hbox {d}t=\kappa _{1}+\kappa _{2}\frac{\hbox {e}^{2\mu \left( 2T-t\right) }\left( 1+\hbox {e}^{-2\gamma \left( T-t\right) }\right) ^{\frac{4}{3}}}{2\mu } \\ S_{2}(t)= & {} \kappa _{1}-\kappa _{2}\frac{\hbox {e}^{2\mu \left( 2T-t\right) }\left( 1+\hbox {e}^{2\gamma \left( t-T\right) }\right) ^{\frac{4}{3}}}{2\mu }. \nonumber \end{aligned}$$
(29)

It remains to determine the two constants of integration. First consider the sales rates on the time interval \(\left[ 0,t^{*}\right] .\) Use the equations

$$\begin{aligned} s_{10}=\kappa _{1}+\kappa _{2}\frac{3\hbox {e}^{\frac{8\gamma }{3}T}\left( 1+\hbox {e}^{-2\gamma T}\right) ^{\frac{4}{3}}}{4\gamma };\quad \text { }s_{20}=\kappa _{1}-\kappa _{2}\frac{3\hbox {e}^{\frac{8\gamma }{3}T}\left( 1+\hbox {e}^{-2\gamma T}\right) ^{\frac{4}{3}}}{4\gamma } \end{aligned}$$

to get

$$\begin{aligned} \kappa _{1}=\frac{s_{10}+s_{20}}{2};\quad \kappa _{2}=\frac{2\gamma \left( s_{10}-s_{20}\right) }{3\hbox {e}^{\frac{8\gamma }{3}}\left( \hbox {e}^{-2T\gamma }+1\right) ^{\frac{4}{3}}} \end{aligned}$$

which provides the sales rate trajectories

$$\begin{aligned} S_{1}\left( t\right)= & {} \frac{s_{10}+s_{20}}{2}+\frac{(s_{10}-s_{20})\hbox {e}^{ \frac{4\gamma }{3}\left( 2T-t\right) }\left( 1+\hbox {e}^{-2\gamma \left( T-t\right) }\right) ^{\frac{4}{3}}}{2\hbox {e}^{\frac{8\gamma }{3}T}\left( \hbox {e}^{-2\gamma T}+1\right) ^{\frac{4}{3}}} \\ S_{2}(t)= & {} \frac{s_{10}+s_{20}}{2}-\frac{(s_{10}-s_{20})\hbox {e}^{\frac{4\gamma }{3 }\left( 2T-t\right) }\left( 1+\hbox {e}^{2\gamma \left( t-T\right) }\right) ^{\frac{4 }{3}}}{2\hbox {e}^{\frac{8\gamma }{3}T}\left( \hbox {e}^{-2\gamma T}+1\right) ^{\frac{4}{3}}} \end{aligned}$$

and their terminal values at time \(t=t^{*}:\)

$$\begin{aligned} S_{1}\left( t^{*}\right)= & {} \frac{s_{10}+s_{20}}{2}+\frac{ (s_{10}-s_{20})\hbox {e}^{\frac{4\gamma }{3}\left( 2T-t^{*}\right) }\left( 1+\hbox {e}^{-2\gamma \left( T-t^{*}\right) }\right) ^{\frac{4}{3}}}{2\hbox {e}^{\frac{ 8\gamma }{3}T}\left( \hbox {e}^{-2\gamma T}+1\right) ^{\frac{4}{3}}} \nonumber \\ S_{2}(t^{*})= & {} \frac{s_{10}+s_{20}}{2}-\frac{(s_{10}-s_{20})\hbox {e}^{\frac{ 4\gamma }{3}\left( 2T-t^{*}\right) }\left( 1+\hbox {e}^{2\gamma \left( t^{*}-T\right) }\right) ^{\frac{4}{3}}}{2\hbox {e}^{\frac{8\gamma }{3}T}\left( \hbox {e}^{-2\gamma T}+1\right) ^{\frac{4}{3}}}. \end{aligned}$$
(30)

The values in (30) serve as initial conditions for the two inhomogeneous equations on the time interval \(\left[ t^{*},T\right] .\) Solutions of the inhomogeneous equations are

$$\begin{aligned} S_{1}\left( t\right)&=\frac{s_{10}+s_{20}}{2}+\frac{\left( s_{10}-s_{20}\right) \hbox {e}^{\frac{4\gamma }{3}\left( 2T-t\right) }\left( 1+\hbox {e}^{-2\gamma \left( T-t\right) }\right) ^{\frac{4}{3}}}{2\hbox {e}^{\frac{8\gamma }{ 3}T}\left( \hbox {e}^{-2T\gamma }+1\right) ^{\frac{4}{3}}} \\&\quad +\left( T\theta +\gamma \eta \right) t-\frac{\theta }{2}t^{2}+\eta \ln \left( \hbox {e}^{-2T\gamma }+1\right) -\eta \ln \left( 1+\hbox {e}^{2\gamma \left( t-T\right) }\right) \\ S_{2}(t)&=\frac{s_{10}+s_{20}}{2}-\frac{\left( s_{10}-s_{20}\right) \hbox {e}^{ \frac{4\gamma }{3}\left( 2T-t\right) }\left( 1+\hbox {e}^{2\gamma \left( t-T\right) }\right) ^{\frac{4}{3}}}{2\hbox {e}^{\frac{8\gamma }{3}T}\left( \hbox {e}^{-2T\gamma }+1\right) ^{\frac{4}{3}}} \\&\quad +\left( T\theta +\gamma \eta \right) t-\frac{\theta }{2}t^{2}+\eta \ln \left( \hbox {e}^{-2T\gamma }+1\right) -\eta \ln \left( 1+\hbox {e}^{2\gamma \left( t-T\right) }\right) \\ \text {for }t&\in \left[ 0,T\right] \end{aligned}$$

and

$$\begin{aligned} S_{1}\left( t\right)&=\frac{s_{10}+s_{20}}{2}+\eta \left( \ln \left( \hbox {e}^{2\gamma \left( t^{*}-T\right) }+1\right) -\ln \left( 1+\hbox {e}^{2\gamma \left( t-T\right) }\right) \right) +\frac{\theta }{2}\left( (t^{*})^{2}-t^{2}\right) \\&\quad +\left( T\theta +\gamma \eta \right) \left( t-t^{*}\right) +\frac{ (s_{10}-s_{20})\hbox {e}^{-\frac{8}{3}T\gamma }\left( \hbox {e}^{2\gamma \left( t-T\right) }+1\right) ^{\frac{4}{3}}\hbox {e}^{-\frac{4}{3}\gamma \left( t-2T\right) }}{2\left( \hbox {e}^{-2T\gamma }+1\right) ^{\frac{4}{3}}} \\ S_{2}(t)&=\frac{s_{10}+s_{20}}{2}+\eta \left( \ln \left( \hbox {e}^{2\gamma \left( t^{*}-T\right) }+1\right) -\ln \left( 1+\hbox {e}^{2\gamma \left( t-T\right) }\right) \right) +\frac{\theta }{2}\left( (t^{*})^{2}-t^{2}\right) \\&\quad +\left( T\theta +\gamma \eta \right) \left( t-t^{*}\right) -\frac{ (s_{10}-s_{20})\hbox {e}^{-\frac{8}{3}T\gamma }\left( \hbox {e}^{2\gamma \left( t-T\right) }+1\right) ^{\frac{4}{3}}\hbox {e}^{-\frac{4}{3}\gamma \left( t-2T\right) }}{2\left( \hbox {e}^{-2T\gamma }+1\right) ^{\frac{4}{3}}} \\ \text {for }t&\in \left[ t^{*},T\right] . \end{aligned}$$

Q.E.D.

Proof of Lemma 1

Introducing constants

$$\begin{aligned} C_{1}=\frac{\lambda ^{2}}{2c_{d}}-\frac{\beta ^{2}}{c_{a}},\, C_{2}= \frac{\beta ^{2}}{2c_{a}}-\frac{\lambda ^{2}}{c_{d}}, \end{aligned}$$

where \(C_{1}<0,C_{1}+C_{2}<0,C_{1}-C_{2}<0,\) the system in (11) can be written as

$$\begin{aligned} \dot{\varphi }(t)&=-m-C_{1}\left( \varphi (t)-\psi (t)\right) ^{2} \end{aligned}$$
(31)
$$\begin{aligned} \dot{\psi }(t)&=-C_{2}\left( \varphi (t)-\psi (t)\right) ^{2}. \end{aligned}$$
(32)

Recall that Case 1 is the one where \(C_{2}>0\) while \(C_{2}<0\) in Case 2\(.\) To solve (31) and (32), we employ a modification of a method used in Bass et al. [2]. Multiplying in (32) by \( -C_{1}/C_{2}\) yields

$$\begin{aligned} -\frac{C_{1}}{C_{2}}\dot{\psi }(t)=C_{1}\left( \varphi (t)-\psi (t)\right) ^{2} \end{aligned}$$

and adding this to (31) provides

$$\begin{aligned} \dot{\varphi }(t)-\frac{C_{1}}{C_{2}}\dot{\psi }(t)=-m. \end{aligned}$$
(33)

Defining \(y(t)=\varphi (t)-C_{1}\psi (t)/C_{2},\) (33) becomes

$$\begin{aligned} \dot{y}(t)=-m\Rightarrow y(t)=-mt+K \end{aligned}$$

and using the condition \(\varphi (T)=\psi (T)=0\) implies \(y(T)=0.\) Therefore

$$\begin{aligned} y(t)=m(T-t). \end{aligned}$$

Then we have

$$\begin{aligned} \varphi (t)=m(T-t)+\frac{C_{1}}{C_{2}}\psi (t) \end{aligned}$$
(34)

which is substituted into (32) to yield

$$\begin{aligned} \dot{\psi }(t)=-C_{2}\left( m(T-t)+\frac{C_{1}-C_{2}}{C_{2}}\psi (t)\right) ^{2}. \end{aligned}$$

This differential equation has the solution

$$\begin{aligned} \psi (t)=\frac{mC_{2}\left( T-t\right) }{C_{2}-C_{1}}-\frac{C_{2}\sqrt{ m\left( C_{2}-C_{1}\right) }}{\left( C_{1}-C_{2}\right) ^{2}}\tanh \sqrt{ m\left( C_{2}-C_{1}\right) }\left( T-t\right) \end{aligned}$$

and substituting \(\psi (t)\) into (34) yields

$$\begin{aligned} \varphi (t)=\frac{mC_{2}\left( T-t\right) }{C_{2}-C_{1}}-\frac{C_{1}\sqrt{ m\left( C_{2}-C_{1}\right) }}{\left( C_{1}-C_{2}\right) ^{2}}\tanh \sqrt{ m\left( C_{2}-C_{1}\right) }\left( T-t\right) . \end{aligned}$$

Q.E.D.

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Jørgensen, S., Sigué, SP. Defensive, Offensive, and Generic Advertising in a Lanchester Model with Market Growth. Dyn Games Appl 5, 523–539 (2015). https://doi.org/10.1007/s13235-015-0147-1

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