A Link Prediction-Based Method Towards Lead Management

  • G. P. Brugalli Universidade Federal de Santa Catarina
  • A. L. Gonçalves Universidade Federal de Santa Catarina
  • A. S. Bordin Universidade Federal de Santa Catarina
  • L. S. Artese Universidade Federal de Santa Catarina

Resumo


Lead management is an essential part of the customer acquisition and retention stages. However, as the number of leads increases, data-driven management automation is critical for better customer acquisition and retention. In this context, the present work proposes a method that supports lead management to identify and recommend to the sales team, future interests of leads that already exist in an organizations database in order to acquire or retain customers. To fulfill this objective, the network representation learning and link prediction models are explored. A case study is presented to demonstrate the effectiveness of the proposed method. All generated models reached a value between 0.873 and 0.998 of ROC-AUC. However, the prediction models showed low coefficient values, far from 1, the ideal value. Nevertheless, the method shows promise to be investigated in practice. For future work, a deep understanding of technical capabilities of network learning is suggested to obtain better results from link prediction models.
Palavras-chave: lead management, network representation learning, link prediction, machine learning

Referências

Amara, A., Taieb, M. A. H., and Aouicha, M. B. Network representation learning systematic review: Ancestors and current development state. Machine Learning with Applications vol. 6, pp. 100130, 2021.

Barabasi, A.-L. and Posfai, M. Network science, 2016.

Dharwal, R. and Kaur, L. Applications of artificial neural networks: a review. Indian J. Sci. Technol 9 (47): 1–8, 2016.

Dong, Y., Chawla, N. V., and Swami, A. metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. pp. 135–144, 2017.

D’Haen, J., Van den Poel, D., and Thorleuchter, D. Predicting customer profitability during acquisition: Finding the optimal combination of data source and data mining technique. Expert systems with applications 40 (6): 2007–2012, 2013.

D’Haen, J., Van den Poel, D., Thorleuchter, D., and Benoit, D. F. Integrating expert knowledge and multi-lingual web crawling data in a lead qualification system. Decision Support Systems vol. 82, pp. 69–78, 2016.

Gebert, H., Geib, M., Kolbe, L., and Riempp, G. Towards customer knowledge management: Integrating customer relationship management and knowledge management concepts, 2002.

Giacosa, E., Culasso, F., and Crocco, E. Customer agility in the modern automotive sector: how lead management shapes agile digital companies. Technological Forecasting and Social Change vol. 175, pp. 121362, 2022.

Grover, A. and Leskovec, J. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 855–864, 2016.

Järvinen, J. and Taiminen, H. Harnessing marketing automation for b2b content marketing. Industrial marketing management vol. 54, pp. 164–175, 2016.

Lee, J., Ko, N., Yoon, J., and Son, C. An approach for discovering firm-specific technology opportunities: Application of link prediction to f-term networks. Technological Forecasting and Social Change vol. 168, pp. 120746, 2021.

Malek, M., Chehreghani, M. H., Nazerfard, E., and Chehreghani, M. H. Shallow node representation learning using centrality indices. In 2021 IEEE International Conference on Big Data (Big Data). IEEE, pp. 5209–5214, 2021.

Martínez, V., Berzal, F., and Cubero, J.-C. A survey of link prediction in complex networks. ACM computing surveys (CSUR) 49 (4): 1–33, 2016.

Michiels, I. Lead lifecycle management: Building a pipeline that never leaks. Tech. rep., Research Report), Aberdeen Group. Retrieved from https://www.ontargetpartners.com/, 2009.

Molontay, R. and Nagy, M. Two decades of network science: as seen through the co-authorship network of network scientists. In Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining. pp. 578–583, 2019.

Monat, J. P. Industrial sales lead conversion modeling. Marketing Intelligence & Planning, 2011.

Ohiomah, A. A., Benyoucef, M., and Andreev, P. Driving inside sales performance with lead management systems: A conceptual model. Journal of Information Systems Applied Research 9 (1): 4, 2016.

Peng, H., Li, J., Yan, H., Gong, Q., Wang, S., Liu, L., Wang, L., and Ren, X. Dynamic network embedding via incremental skip-gram with negative sampling. Science China Information Sciences 63 (10): 1–19, 2020.

Sabnis, G., Chatterjee, S. C., Grewal, R., and Lilien, G. L. The sales lead black hole: On sales reps’ follow-up of marketing leads. Journal of marketing 77 (1): 52–67, 2013.

Vespignani, A. and Caldarelli, G. Large scale structure and dynamics of complex networks: from information technology to finance and natural science. Vol. 2. World scientific, 2007.

Wu, Y.-c. and Feng, J.-w. Development and application of artificial neural network. Wireless Personal Communications 102 (2): 1645–1656, 2018.

Yu, Y.-P. and Cai, S.-Q. A new approach to customer targeting under conditions of information shortage. Marketing intelligence & planning, 2007.
Publicado
28/11/2022
BRUGALLI, G. P.; GONÇALVES, A. L.; BORDIN, A. S.; ARTESE, L. S.. A Link Prediction-Based Method Towards Lead Management. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 10. , 2022, Campinas/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 122-129. ISSN 2763-8944. DOI: https://doi.org/10.5753/kdmile.2022.227800.