Purpose
This article is intended to provide an overview of Allbound's Suggested Action Generation Engine (SAGE) Content Scoring feature to System and Content Admins.
Introduction
In the realm of partner relationship management (PRM) and channel sales, effective content can influence closed won deals and drive revenue. Our machine learning initiative is designed to gauge the correlation between content and its potential impact on revenue. We've run content engagement actions and closed won deal data through our machine learning model to score each piece of content. By providing this scoring in the Content Management Tool, we offer content administrators a clear compass on which content pieces are pivotal in engaging their audience and facilitating closed won deals.
Revenue Impact: Deal Value
Based on closed won deals, this model predicts that anticipated impact a piece of a content has on the Deal Value for Closed Won deals. This score is useful for non-Referral partners that track a deal's estimated amount in the partner portal.
- Significant - Content scored as Significant may have a very positive impact on the deal value of closed won deals.
- Moderate - Content scored as Moderate may have less impact than content scored as Significant.
- Neutral - Content scored as Neutral may have negligible impact compared to content scored as Significant or Moderate.
Revenue Impact: Deal Count
Based on closed won deals, this model predicts that anticipated impact a piece of a content has on the number of Closed Won deals. This score is useful for Referral partners that don't track a deal's estimated amount in the partner portal.
- Significant - Content scored as Significant may have a very positive impact on the total number of closed won deals.
- Moderate - Content scored as Moderate may have less impact than content scored as Significant.
- Neutral - Content scored as Neutral may have negligible impact compared to content scored as Significant or Moderate.
Next Steps
Leveraging these insights allows content administrators to strategically prioritize and promote content. Significant-impact pieces, as indicated by our models, are given the emphasis they warrant, ensuring they reach the intended audiences effectively. By centering attention on significant-impact content, businesses foster deeper engagement with their partners. This ensures that these stakeholders interact with content that not only resonates but also has a proven potential to facilitate successful deals.
*Note: These models are predictive and their outputs should be considered as a guiding starting point, rather than definitive outcomes. They are part of an iterative process that's expected to evolve, improve, and become more refined over time. Customer feedback and continuous evaluation will play a crucial role in this enhancement process. The implementation of a feedback loop in the future is crucial for fine-tuning and enhancing the model's accuracy.
If you have feedback on our Content Scoring feature, please reach out to your CSM or email feedback@allbound.com.