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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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Funding

This study was not funded.

Author information

Authors and Affiliations

  1. Department of CSE, BPPIMT, Kolkata, India

    Soumi Tokdar & Ananya Kanjilal

  2. Department of CSE, University of Calcutta, Kolkata, India

    Sankhayan Choudhury

  3. Deparment of CSE, Jadavpur University, Kolkata, India

    Swapan Bhattacharya

Authors
  1. Soumi Tokdar
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  2. Ananya Kanjilal
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  3. Sankhayan Choudhury
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  4. Swapan Bhattacharya
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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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