The Sethi advertising model or simply the Sethi model provides a sales-advertising dynamics in the form of the following
stochastic differential equation: : dX_t =\left(rU_t\sqrt{1-X_t} - \delta X_t\right)\,dt+\sigma(X_t)\,dz_t, \qquad X_0=x. Where: • X_t is the market share at time t • U_t is the rate of advertising at time t • r is the coefficient of the effectiveness of advertising • \delta is the decay constant • \sigma(X_t) is the diffusion coefficient • z_t is the
Wiener process (Standard
Brownian motion); dz_t is known as
White noise.
Explanation The rate of change in sales depend on three effects: response to advertising that acts positively on the unsold portion of the market via r, the loss due to forgetting or possibly due to competitive factors that act negatively on the sold portion of the market via \delta, and a random effect using a diffusion or White noise term that can go either way. • The coefficient r is the coefficient of the effectiveness of advertising innovation. • The coefficient \delta is the decay constant. • The square-root term brings in the so-called word-of-mouth effect at least at low sales levels. : V(x)=\bar\lambda x+ \frac{\bar\lambda^2 r^2}{4 \rho}, where : \bar\lambda=\frac{\sqrt{(\rho+\delta)^2+r^2 \pi}-(\rho+\delta)}{r^2/2}. The
optimal control for this problem is : U^*_t = u^*(X_t)=\frac{r\bar\lambda \sqrt{1-\ X_t}}{2} = \begin{cases} {} > \bar{u} & \text{if } X_t \bar{x}, \end{cases} where : \bar x= \frac{r^2 \bar\lambda /2}{r^2 \bar\lambda /2+\delta} and : \bar u=\frac{r\bar\lambda \sqrt{1-\bar x}}{2}. ==Extensions of the Sethi model==