Optimizing Trial Experiences in Cloud Platforms: Challenges, Strategies, and Impact on User Engagement and Conversion Rate
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This article delves into the significance of trial experiences in the context of cloud-based solutions, a crucial aspect of today's digital business landscape. As cloud platforms reshape service delivery, the Freemium and Free Trial models emerge as compelling strategies for customer engagement. These models not only offer a glimpse into the service's capabilities but also serve as a critical touchpoint for building trust and rapport with potential customers. Optimizing trial experiences, however, comes with a set of challenges, including balancing feature accessibility with the need to incentivize paid conversions and tailoring the trial to diverse user needs and expectations. This article extensively examines the optimization of trial experiences within cloud platforms, encompassing hurdles, strategic approaches, and their profound influence on user engagement and conversion rates. It highlights the delicate art of designing trial experiences that are sufficiently feature-rich to demonstrate value yet limited enough to encourage upgrade to paid versions. The article also discusses how personalization and customer feedback can be leveraged to enhance trial experiences. By analyzing key industry players like Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP), the article sheds light on how these frontrunners utilize trial experiences to captivate audiences and fortify their customer base. It explores their distinct approaches in offering trials, the impact on market positioning, and how they balance the need for security and compliance with user accessibility. The insights from these industry giants provide valuable lessons for other players in the cloud computing sphere looking to harness the power of trial experiences for customer acquisition and retention.
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B. Ahn and H. Ahn, “Factors Affecting Intention to Adopt Cloud-Based ERP from a Comprehensive Approach,” Sustainability, vol. 12, no. 16, p. 6426, Aug. 2020, doi: https://doi.org/10.3390/su12166426.
X. Liu, C.-W. Liu, S. Jiang, and J. Duan, “Does Less Mean More? A Large-Scale Randomized Field Experiment Investigating the Impact of Vip Label Absence on User Conversion in Freemium Platforms,” Social Science Research Network, Jan. 2023, doi: https://doi.org/10.2139/ssrn.4472561.
M. Schreieck, M. Wiesche, and H. Krcmar, “EXPRESS: Capabilities for Value Co-Creation and Value Capture in Emergent Platform Ecosystems: A Longitudinal Case Study of SAP’s Cloud Platform,” Journal of Information Technology, p. 026839622110237, Jun. 2021, doi: https://doi.org/10.1177/02683962211023780.
A. Mavromatis, C. Colman-Meixner, A. P. Silva, X. Vasilakos, R. Nejabati, and D. Simeonidou, “A Software-Defined IoT Device Management Framework for Edge and Cloud Computing,” IEEE Internet of Things Journal, vol. 7, no. 3, pp. 1718–1735, Mar. 2020, doi: https://doi.org/10.1109/jiot.2019.2949629.
S. E. Sampson and R. B. Chase, “Optimizing Customer Involvement: How Close Should You Be to Your Customers?,” California Management Review, vol. 65, no. 1, p. 000812562211181, Oct. 2022, doi: https://doi.org/10.1177/00081256221118117.
S. Bag, G. Srivastava, M. M. A. Bashir, S. Kumari, M. Giannakis, and A. H. Chowdhury, “Journey of customers in this digital era: Understanding the role of artificial intelligence technologies in user engagement and conversion,” Benchmarking: An International Journal, vol. ahead-of-print, no. ahead-of-print, Sep. 2021, doi: https://doi.org/10.1108/bij-07-2021-0415.
] R. Watson, “The Virtual Economy of the Metaverse: Computer Vision and Deep Learning Algorithms, Customer Engagement Tools, and Behavioral Predictive Analytics,” www.addletonacademicpublishers.com. https://www.addletonacademicpublishers.com/contents-lpi/2445-volume-21-2022/4237-the-virtual-economy-of-the-metaverse-computer-vision-and-deep-learning-algorithms-customer-engagement-tools-and-behavioral-predictive-analytics (accessed Aug. 28, 2023).
M. F. Dockendorf, B. J. Hansen, K. P. Bateman, M. Moyer, J. K. Shah, and L. A. Shipley, “Digitally Enabled, Patient‐Centric Clinical Trials: Shifting the Drug Development Paradigm,” Clinical and Translational Science, vol. 14, no. 2, pp. 445–459, Nov. 2020, doi: https://doi.org/10.1111/cts.12910.
S. Okai-Ugbaje, K. Ardzejewska, A. Imran, A. Yakubu, and M. Yakubu, “Cloud-Based M-Learning: A Pedagogical Tool to Manage Infrastructural Limitations and Enhance Learning,” International Journal of Education and Development using Information and Communication Technology, vol. 16, no. 2, pp. 48–67, 2020, Available: https://eric.ed.gov/?id=EJ1268798.
Y. Maleh, Y. Baddi, M. Alazab, L. Tawalbeh, and I. Romdhani, Eds., Artificial Intelligence and Blockchain for Future Cybersecurity Applications. Cham: Springer International Publishing, 2021. doi: https://doi.org/10.1007/978-3-030-74575-2.
T. A. Davenport et al., “Validation of the InnoWell Platform: Protocol for a Clinical Trial,” JMIR Research Protocols, vol. 8, no. 5, p. e13955, May 2019, doi: https://doi.org/10.2196/13955.
E. Bisong, Building Machine Learning and Deep Learning Models on Google Cloud Platform. Berkeley, CA: Apress, 2019. doi: https://doi.org/10.1007/978-1-4842-4470-8.
P. Pierleoni, R. Concetti, A. Belli, and L. Palma, “Amazon, Google and Microsoft Solutions for IoT: Architectures and a Performance Comparison,” IEEE Access, vol. 8, pp. 5455–5470, 2020, doi: https://doi.org/10.1109/access.2019.2961511.
Fattah, S. M. M., Bouguettaya, A., & Mistry, S. (2019, July). Long-term iaas provider selection using short-term trial experience. In 2019 IEEE International Conference on Web Services (ICWS) (pp. 304-311). IEEE.
S. M. M. Fattah, A. Bouguettaya, and S. Mistry, “Signature-based Selection of IaaS Cloud Services,” 2020 IEEE International Conference on Web Services (ICWS), Oct. 2020, doi: https://doi.org/10.1109/icws49710.2020.00014.
N. J. Mitchell and K. Zunnurhain, “Google cloud platform security,” Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, Nov. 2019, doi: https://doi.org/10.1145/3318216.3363371.
H. Yu, M. K. Afzal, Y. B. Zikria, A. Rachedi, and F. H. P. Fitzek, “Tactile Internet: Technologies, test platforms, trials, and applications,” Future Generation Computer Systems, vol. 106, pp. 685–688, May 2020, doi: https://doi.org/10.1016/j.future.2020.01.057.
“Prospects for Artificial Intelligence (AI) Implementation to Design Personalized Customer Engagement Strategies,” kjsim.somaiya.edu. https://kjsim.somaiya.edu/en/view-publication/371?type=proceeding&id=170060 (accessed Aug. 28, 2023)
Behera, R. K., & Dhal, S. K. (2020). The Impact of ERP Systems on financial Performance of Central Public Sector Enterprises Working in Mineral and Metal Sector. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 9, Issue 2, pp. 144–149). https://doi.org/10.35940/ijrte.b3287.079220
Kaushik, D. S., & Bhardwaj, Dr. A. (2020). Compressing Effort a nd Schedule i n ERP Implementations w ith Iterative Methodologies : Few Industrial Cases. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 5, pp. 1061–1067). https://doi.org/10.35940/ijitee.e2244.039520
Wei*, K. S., Jabar, M. A., Abdullah, R. H., & Murad, M. A. A. (2019). An Exploratory Performance of Risks Factors for Pre-Implementation of ERP Project in Malaysia. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 1, pp. 3452–3455). https://doi.org/10.35940/ijeat.a2660.109119
Patel, Dr. M., & Jaiswal, M. B. (2020). The Learning on CRM in ERP- with Special References to Selected Engineering Companies in Gujarat. In International Journal of Management and Humanities (Vol. 4, Issue 8, pp. 117–126). https://doi.org/10.35940/ijmh.h0798.044820
Wanjau, S. K., Wambugu, G. M., & Oirere, A. M. (2022). Network Intrusion Detection Systems: A Systematic Literature Review of Hybrid Deep Learning Approaches. In International Journal of Emerging Science and Engineering (Vol. 10, Issue 7, pp. 1–16). https://doi.org/10.35940/ijese.f2530.0610722