In this seminar, Thao L e will give a talk about Growth in Saturated Market: A Markov-based N etwork Optimization Approach.
In mature markets where total de mand is fixed, competitive growth depends not only on attracting cust omers but also on the timing of their purchases. Yet most quanti tative marketing models--whether probabilistic choice, state-dependen t demand, or network-based diffusion--capture which opt ion consumers switch to but not when switching occurs, despite substantial heterogeneity in purchase timing driven by fricti ons, habits, and depletion dynamics.
We address thi s gap by introducing a heterogeneous-time Markov network that jointly models probabilistic and temporal transitions in purchase behavior. Methodologically, we derive closed-form expressions for market share, sales rates, and inter-purchase times in this heterogeneous-time env ironment, enabling analytical tractability without distributional ass umptions or simulation. Building on these results, we develop a gradi ent-based optimization framework that respects fixed market demand an d identifies minimal, targeted interventions for shifting share. Usin g IRI panel data, we demonstrate that jointly optimizing switching pr obabilities and switching times yields significantly larger gains and requires smaller deviations from observed behavior than probability- only approaches.
Our work provides a modern q uantitative framework for understanding and optimizing competitive dy namics in mature, zero-sum markets, and highlights the role of tempor al heterogeneity as an underexplored driver of firm performance.
DESCRIPTION: In mature markets where total demand is fixed, competitiv e growth depends not only on attracting customers but also on the tim ing of their purchases. Yet most quantitative marketing models-- whether probabilistic choice, state-dependent demand, or network-base d diffusion--capture which option consumers switch to b ut not when switching occurs, despite substantial heter ogeneity in purchase timing driven by frictions, habits, and depletio n dynamics. We address this gap by introducing a heteroge neous-time Markov network that jointly models probabilistic and tempo ral transitions in purchase behavior. Methodologically, we derive clo sed-form expressions for market share, sales rates, and inter-purchas e times in this heterogeneous-time environment, enabling analytical t ractability without distributional assumptions or simulation. Buildin g on these results, we develop a gradient-based optimization framewor k that respects fixed market demand and identifies minimal, targeted interventions for shifting share. Using IRI panel data, we demonstrat e that jointly optimizing switching probabilities and switching times yields significantly larger gains and requires smaller deviations fr om observed behavior than probability-only approaches. � � Our work provides a modern quantitative framework for understandi ng and optimizing competitive dynamics in mature, zero-sum markets, a nd highlights the role of temporal heterogeneity as an underexplored driver of firm performance. In this seminar, Thao Le will give a talk about Growth in Saturated Market: A Markov-based Network Optimizatio n Approach. END:VEVENT END:VCALENDAR