We have continuously used market adaptability, product alterations, and altered service modalities to engaged the market. One may say that these are all reactive strategies. Can AI and machine learning avoid these reactive steps to market by enhancing customer predictability?
Hi Zameer,
This is a nice question. Please follow some of the papers below to understand the applicability of ML:
1. Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), 1140-1154.
2. Sharma, N., Sharma, P., Irwin, D., & Shenoy, P. (2011, October). Predicting solar generation from weather forecasts using machine learning. In Smart Grid Communications (SmartGridComm), 2011 IEEE International Conference on(pp. 528-533). IEEE.
3. Rapach, D., Strauss, J., Tu, J., & Zhou, G. (2018). Dynamic Return Dependencies Across Industries: A Machine Learning Approach.
4. Gordini, N., & Veglio, V. (2017). Customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry. Industrial Marketing Management, 62, 100-107.
5. Bhandari, A., Rama, K., Seth, N., Niranjan, N., Chitalia, P., & Berg, S. (2017, July). Towards an efficient method of modeling “Next Best Action” for Digital Buyer’s journey in B2B. In International Conference on Machine Learning and Data Mining in Pattern Recognition (pp. 107-116). Springer, Cham.
6. Samanpour, A. R., Ruegenberg, A., & Ahlers, R. (2018). The Future of Machine Learning and Predictive Analytics. In Digital Marketplaces Unleashed (pp. 297-309). Springer, Berlin, Heidelberg.
7. Roosevelt, T. (2018). How to Increase Customer Acquisition and Retention with UDA. Unstructured Data Analytics: How to Improve Customer Acquisition, Customer Retention, and Fraud Detection and Prevention, 109.
Thanks,
Sobhan
Hi Zameer,
This is a nice question. Please follow some of the papers below to understand the applicability of ML:
1. Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), 1140-1154.
2. Sharma, N., Sharma, P., Irwin, D., & Shenoy, P. (2011, October). Predicting solar generation from weather forecasts using machine learning. In Smart Grid Communications (SmartGridComm), 2011 IEEE International Conference on(pp. 528-533). IEEE.
3. Rapach, D., Strauss, J., Tu, J., & Zhou, G. (2018). Dynamic Return Dependencies Across Industries: A Machine Learning Approach.
4. Gordini, N., & Veglio, V. (2017). Customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry. Industrial Marketing Management, 62, 100-107.
5. Bhandari, A., Rama, K., Seth, N., Niranjan, N., Chitalia, P., & Berg, S. (2017, July). Towards an efficient method of modeling “Next Best Action” for Digital Buyer’s journey in B2B. In International Conference on Machine Learning and Data Mining in Pattern Recognition (pp. 107-116). Springer, Cham.
6. Samanpour, A. R., Ruegenberg, A., & Ahlers, R. (2018). The Future of Machine Learning and Predictive Analytics. In Digital Marketplaces Unleashed (pp. 297-309). Springer, Berlin, Heidelberg.
7. Roosevelt, T. (2018). How to Increase Customer Acquisition and Retention with UDA. Unstructured Data Analytics: How to Improve Customer Acquisition, Customer Retention, and Fraud Detection and Prevention, 109.
Thanks,
Sobhan
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