Modeling dynamic effects of promotion on interpurchase times

In this paper we put forward a duration model to analyze the dynamic effects of marketing-mix variables on interpurchase times. We extend the accelerated failure-time model with an autoregressive structure. An important feature of our model is that it allows for different long-run and short-run effects of marketing-mix variables on interpurchase times. As marketing efforts usually change during the spells, we explicitly deal with time-varying covariates. Our empirical analysis of purchases in three different categories reveals that, for some segments of households, the short-run effects of marketing-mix variables are significantly different from the long-run effects.Dynamic duration model;Error-correction model;Time-varying covariates;Unobserved heterogeneity

Modeling dynamic effects of promotion on interpurchase times

In this paper we put forward a duration model to analyze the dynamic effects of marketing-mix variables on interpurchase times. We extend the accelerated failure-time model with an autoregressive structure. An important feature of our model is that it allows for different long-run and short-run effects of marketing-mix variables on interpurchase times. As marketing efforts usually change during the spells, we explicitly deal with time-varying covariates. Our empirical analysis of purchases in three different categories reveals that, for some segments of households, the short-run effects of marketing-mix variables are significantly different from the long-run effects

Modeling purchases as repeated events

We put forward a statistical model for interpurchase times that takes into account all the current and past information available for all purchases as time continues to run along the calendar timescale. It delivers forecasts for the number of purchases in the next period and for the timing of the first and consecutive purchases. Purchase occasions are modeled in terms of a counting process, which counts the recurrent purchases for each household as they evolve over time. We show that formulating the problem as a counting process has many advantages, both theoretically and empirically. We illustrate our model for yogurt purchases and we highlight its useful managerial implications

The Price Consideration Model of Brand Choice

The workhorse brand choice models in marketing are the multinomial logit (MNL) and nested multinomial logit (NMNL). These models place strong restrictions on how brand share and purchase incidence price elasticities are related. In this paper, we propose a new model of brand choice, the “price consideration” (PC) model, that allows more flexibility in this relationship. In the PC model, consumers do not observe prices in each period. Every week, a consumer decides whether to consider a category. Only then does he/she look at prices and decide whether and what to buy. Using scanner data, we show the PC model fits much better than MNL or NMNL. Simulations reveal the reason: the PC model provides a vastly superior fit to inter-purchase spells.Brand Choice; Purchase Incidence; Price Elasticity; Inter-purchase Spell

Regularity in individual shopping trips: Implications for duration models in marketing

Most models for purchase timing behavior of households do not take into account that many households have regular and non-shopping days. I propose a statistical model for purchase timing that exploits information on the shopping days of households. It delivers forecasts for the number of purchases in the next period and for the timing of the first and consecutive purchases. Purchase occasions are modeled in terms of a counting process, which counts the recurrent purchases for each household as they evolve over time. I illustrate the model for yogurt and detergent purchases and highlight its useful managerial implications