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Promotional Predictive Marketing: User Centric Data Driven Approach

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Abstract

This paper proposes value addition to the classical Influence Maximization problem by introducing a quality measure to the participating nodes. The quality measure signifies the ‘propensity to buy’ of a customer (node) in a promotional marketing campaign context. Two metrics, Individual Net Worth (INW) and Neighborhood Net Worth (NNW) are proposed to measure the potential of a customer(s) in buying a given product. The proposed solution, through a heuristic approach, is capable of spreading the influence to the customers with a higher propensity to buy the product. The solution is scalable and adaptable to address user requirements. All these claims are substantiated through experimental results on public datasets. We performed a comparative study with notable algorithms in this domain. The result shows that the proposed approach selects seeds of higher quality as well as maximizes the overall quality (worth) of the influenced nodes in comparison to the notable algorithms, without any adverse impact on time complexity.

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Availability of Data and Material

Public datasets are used. The sources of the datasets are mentioned in the dataset Sect. 6.1 in the manuscript.

Code Availability

Custom code developed using python for experimental validation.

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This study was not funded.

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Correspondence to Soumi Tokdar.

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Appendix 1: Comparison of seed node set for varying weight values

Appendix 1: Comparison of seed node set for varying weight values

W (weights for diff attributes) Algorithm Seed set (Node numbers) Diff. in seeds Diff %
W1 = (0,0,0.33,0.67) AWMIS (proposed approach) 1441, 3423, 6142, 6948, 7859, 13648, 14017, 14726, 16164, 19615, 23282, 23420, 24059, 26122, 28950, 29928, 30160, 30744, 33512, 34787, 36103, 39085, 44262, 53601, 54465, 63113, 63697, 65168, 65553, 68111 Benchmark for comparison
W2 = (0,0.5,0.5,0) 97, 1441, 4436, 6142, 6948, 7859, 13648, 14726, 17289, 17370, 19615, 23282, 23420, 26122, 28950, 29928, 30744, 33512, 34787, 36103, 39085, 44262, 54465, 60926, 63113, 63697, 65168, 65553, 66349, 68111 6 20%
W3 = (0.33,0.67,0,0) 1441, 3423, 4436, 6142, 6948, 7233, 7859, 13648, 14017, 14726, 16164, 17370, 17793, 19615, 23282, 23420, 26122, 28950, 30160, 30744, 33512, 36103, 39085, 44262, 53601, 54465, 63113, 63697, 65168, 68111 4 13.33%
W4 = (0.5,0,0, 0.5) 1441, 3423, 4436, 6142, 6948, 7859, 13648, 14017, 14726, 16164, 17370, 17793, 19615, 23282, 23420, 26122, 28950, 30160, 30744, 33512, 36103, 39085, 44262, 48973, 53601, 54465, 63113, 63697, 65168, 68111 4 13.33%
W1 = (0,0,0.33,0.67) CELF 97, 1441, 3423, 4436, 6142, 6948, 7859, 14017, 14726, 16164, 17370, 17793, 19615, 20394, 23282, 23420, 28950, 29715, 30744, 33512, 36103, 39085, 43684, 44262, 54465, 62227, 63113, 63697, 65168, 68111 No change
W2, W3, W4 Same seed set. No change due to change in W
W1, W2, W3, W4 DD Same seed set. No change due to change in W No change
W1, W2, W3, W4 ORIE Same seed set. No change due to change in W No change
  1. *Nodes in bold are new nodes selected in the seed set replacing the old ones based on varying W values

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Tokdar, S., Kanjilal, A., Choudhury, S. et al. Promotional Predictive Marketing: User Centric Data Driven Approach. SN COMPUT. SCI. 3, 444 (2022). https://doi.org/10.1007/s42979-022-01342-3

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  • DOI: https://doi.org/10.1007/s42979-022-01342-3

Keywords

  • Target influence maximization
  • Heuristic-based seed selection
  • Customer net worth
  • Promotional predictive marketing