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spending more than a new incremental(ly lower profit) customer will ever be worth. Creating value from customers is an optimization problem. Often, however, the tradeoffs occur in terms of generating increased future cash flows with adverse results of reduced current cash flows, or vice-versa. . . . If CLV doesn t improve enough to offset increased costs, then value will be destroyed, even as loyalty improves.

where Rg(n - m) = - g(t - nTs)g*(t - mTs)dt and qn = - y(t)g*(t - nTs)dt is the matched- lter output (with impulse response g*(-t)) of the received signal y(t) (sampled at t = nTs). Observe that the ML metric m(v, w) of w in Equation (1.37) depends on the received signal y(t) only through qn = - y(t)g(t - nTs)dt. Once q = [q1, . . . , qN] are known, the ML metric m(v, w) can be computed. Hence, the matched- lter outputs, qn = - y(t)g(t - nTs)dt, are also the suf cient statistics with respect to w. Therefore, the ML metric m(v, w) in Equation (1.37) can be expressed in terms of the suf cient statistics q = [q1, . . . , qN] directly as m(q, w). If g(t) = 0 for all t > Ts or t < 0 [zero intersymbol interference (ISI) due to the transmit pulseshape g(t)], we have - g(t - nTs)g*(t - mTs)dt = d(n - m) and the ML metric m(q, w) can be further simpli ed as * * m (q, w ) = xn (w )hn qn - hn xn (w )

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Each link i has a queue associated with it. For simplicity, we assume that both the arrival and the departure processes of the queue are speci ed in bit level and that a source bit arrives at link i according to the Possion arrivals at a rate of li [bits per second (bps)]. When the source bit arrives at link i, it is appended to the tail of the queue. When a set of links A is activated at a timestep, the head of the queue of each link in A is removed. We have the following necessary and suf cient condition on the stability of the system. Theorem 11.1 (Condition for Stability for the Poisson Arrivals) If there is a scheduling algorithm for which the system is stable, then the rate vector of the Poisson arrival (bps) l = [l1, . . . , lK] is strictly dominated by a convex combination of the rate vectors r = [r1, . . . , rK] in the capacity region C. Conversely, if l is strictly dominated by a convex combination of the rate vectors in the capacity region C, then there exists a scheduling algorithm for which the system is stable. Proof Please refer to Appendix 11A. Theorem 11.1 provides a necessary and suf cient condition for the stability of systems with Poisson arrivals. For instance, the stability region of the system S can be written as S = l = (l1 , . . . , l K ) : l a n rn for some rn C and a n 0 n such that a n = 1 n

(11.14)

All spreadsheets can help in the same way. You can play what-if until the cows come home or at least until the boss allocates another sliver of the budget for a Facebook app that might go viral. The solution is to get a clear handle on your goals and sweep up a handful of online metrics tools and services to prove your point. Show the actual results of your endeavors rather than hockey-stick conjectures. Just don t go overboard. That ll lead you to where the tool-price quandary pendulum swings too far the other way.

By integrating customer relationship management with shareholder wealth creation and destruction, both of these topics are placed into the context of performance management.

where the operator refers to componentwise comparison. Since Snanrn represents a convex combination of all the feasible rate vectors rn in the capacity region of the time-invariant physical layer, the stability region can be written as S = int(C ) (11.15)

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