Collective Response Spike Prediction for Mutually Interacting Consumers

Takahashi, Rikiya; Mizuta, Hideyuki; Abe, Naoki; Kennedy, Ruby L.; Jeffs, Vincent J.; Shah, Riddhi; Crites, Robert H.

doi:10.1109/icdm.2013.84

Cited by 3 publications

(11 citation statements)

References 30 publications

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“…Thus the NBD model says nothing about when to communicate with a customer. Some extensions of the NBD model have been proposed, although to maintain model tractability, they are based on unrealistic assumptions such that the purchase behavior only depends on the last purchase decision, and that the effect of marketing variables is piecewise constant over time [5]- [12]. Our analysis finds that these assumptions are violated in actual data.…”

Section: Introductionmentioning

confidence: 60%

“…A natural extension of this study is to extend our model to incorporate the interaction among customers [12], [25] or among items [2], which is expected to improve its predictive performance. HTRm is not limited to purchase event data, but is widely applicable to any sequences of event points.…”

Section: Discussionmentioning

confidence: 99%

“…Inclusion of Covariate: It was also proposed to incorporate time-varying covariates such as seasonality and price promotion into the renewal model, in which the survivor function can be obtained analytically, as long as the piecewise constancy of covariates is assumed [7]- [9], [12].…”

Section: Previous Workmentioning

confidence: 99%

See 2 more Smart Citations

Analyzing Temporal Dynamics of Consumer's Behavior Based on Hierarchical Time-Rescaling

Kim

Takaya

Sawada

2017

IEICE Trans. Inf. & Syst.

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SUMMARYImprovements in information technology have made it easier for industry to communicate with their customers, raising hopes for a scheme that can estimate when customers will want to make purchases. Although a number of models have been developed to estimate the timevarying purchase probability, they are based on very restrictive assumptions such as preceding purchase-event dependence and discrete-time effect of covariates. Our preliminary analysis of real-world data finds that these assumptions are invalid: self-exciting behavior, as well as marketing stimulus and preceding purchase dependence, should be examined as possible factors influencing purchase probability. In this paper, by employing the novel idea of hierarchical time rescaling, we propose a tractable but highly flexible model that can meld various types of intrinsic history dependency and marketing stimuli in a continuous-time setting. By employing the proposed model, which incorporates the three factors, we analyze actual data, and show that our model has the ability to precisely track the temporal dynamics of purchase probability at the level of individuals. It enables us to take effective marketing actions such as advertising and recommendations on timely and individual bases, leading to the construction of a profitable relationship with each customer.

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Section: Introductionmentioning

confidence: 60%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Analyzing Temporal Dynamics of Consumer's Behavior Based on Hierarchical Time-Rescaling

Kim

Takaya

Sawada

2017

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

show abstract

“…Thus the NBD model says nothing about when to communicate with a customer. Some extensions of the NBD model have been proposed, although to maintain model tractability, they are based on unrealistic assumptions such as the purchase behavior only depends on the last purchase decision, and that the effect of marketing variables is piecewise constant over time [6,16,10,1,12,14,22,21]. Our analysis finds that these assumptions are violated in actual data.…”

Section: Introductionmentioning

confidence: 77%

Section: Previous Workmentioning

confidence: 99%

Tracking Temporal Dynamics of Purchase Decisions via Hierarchical Time-Rescaling Model

Kim

Takaya

Sawada

2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

View full text Add to dashboard Cite

Improvements in information technology have made it easier for industry to communicate with their customers, raising hopes for a scheme that can estimate when customers will want to make purchases. Although a number of models have been developed to estimate the time-varying purchase probability, they are based on very restrictive assumptions such as preceding purchase-event dependence and discrete-time effect of covariates. Our preliminary analysis of real-world data finds that these assumptions are invalid: self-exciting behavior, as well as marketing stimulus and preceding purchase dependence, should be examined as possible factors influencing purchase probability. In this paper, by employing the novel idea of hierarchical time rescaling, we propose a tractable but highly flexible model that can meld various types of intrinsic history dependency and marketing stimuli in a continuous-time setting. By employing the proposed model, which incorporates the three factors, we analyze actual data, and show that our model has the ability to precisely track the temporal dynamics of purchase probability at the level of individuals. It enables us to take effective marketing actions such as advertising and recommendations on timely and individual bases, leading to the construction of a profitable relationship with each customer.

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Multi-period marketing-mix optimization with response spike forecasting

Takahashi¹,

Yoshizumi²,

Mizuta³

et al. 2014

IBM J. Res. & Dev.

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Optimizing the marketing budget and its distribution across various channels and target segments, along with the timing of each marketing action for maximum profit, are among the essential problems in marketing. A crucial requirement for effective decision making in direct marketing is an efficient optimization algorithm, coupled with reliable predictions on how the various marketing actions will impact consumers' purchase decisions. For maximizing mid-term revenues, while properly incorporating the temporal dynamics of individual consumers' mental states, we introduce a comprehensive optimization framework for obtaining a state-based marketing-mix policy, with a novel combination of machine learning, optimization, and simulation. Each consumer's purchase decision is modeled with a marked point process whose discrete-state approximation enables efficient optimization of high-dimensional decision variables, and whose discrete event simulation supplies accurate forecasts of the revenues and risks of the optimized policy.The key idea for ensuring the practicality of the proposed approach to marketing planning is a linear programming formulation that allows both handling of temporal dynamics and complex budget constraints across many periods, segments, and channels required in real marketing operations. A real-world case study based on an online retailing dataset implies high revenue gains when the simulation model can be relied on.

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Collective Response Spike Prediction for Mutually Interacting Consumers

Cited by 3 publications

References 30 publications

Analyzing Temporal Dynamics of Consumer's Behavior Based on Hierarchical Time-Rescaling

Analyzing Temporal Dynamics of Consumer's Behavior Based on Hierarchical Time-Rescaling

Tracking Temporal Dynamics of Purchase Decisions via Hierarchical Time-Rescaling Model

Multi-period marketing-mix optimization with response spike forecasting

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