The rise of social media has significantly influenced digital marketing, especially within the
IT Channel Industry. Channext, a key player in automating channel marketing, faces the
challenge of achieving optimal through-partner social engagement, which includes maximiz-
ing shares, impressions, clicks, likes, and comments on social media posts. However, the
current approach is hindered by content overload, leading to scheduling conflicts and reduced
engagement.

To address this issue, this project introduces an advanced model for the vendor-partner net-
work, with several key contributions:
• Predictive Analytics: A robust predictor is developed to capture the relationship be-
tween historical impressions data and social post characteristics, aiming to forecast
future impressions with improved accuracy.
• Optimized Scheduling: Predictive insights guide the scheduling model to maximize
impressions while minimizing post overlaps and balancing visibility across the network.
• Real-Time Adaptation: The model incorporates real-time impressions data to refine
predictions, enabling the system to dynamically adjust to fluctuations in engagement
trends.
• Future Expansion: Building on this model, the goal is to integrate a learning system
based on the linUCB algorithm, balancing exploration and exploitation to allow the
model to better adapt to the evolving dynamics of social media.

Driven by carefully selected data and machine learning techniques, this framework aims to
enhance through-partner social engagement. By empowering IT partners to actively participate in vendor-led social media campaigns while avoiding over-publishing, we seek to optimize
engagement strategies.
Furthermore, the integration of a learning framework aims to enable the project to autonomously adapt to changes in the vendor-partner network’s dynamics. Achieving this level of adaptability will require the development of methods to handle non-stationary environ-
ments, where engagement patterns evolve over time. With these advancements, the project
could set a new standard for intelligent, responsive marketing strategies in the IT channel
industry. Moving beyond traditional automation, this project envisions a finely tuned system
capable of sustaining impact in the rapidly evolving digital landscape.