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Empowering Support Agents with Next Best Action Recommendations
Introduction Methodology Solution Architecture Reaction and feedback Conclusion Key takeaways
Key takeaways
References

Key takeaways

Home > AI Solutions > Artificial Intelligence > White Papers > Empowering Support Agents with Next Best Action Recommendations > Key takeaways

Key takeaways

  • The key takeaways of this white paper include:

    • NBA standardizes the troubleshooting process
    • This solution functions irrespective of agents experience and expertise in resolving issues. NBA focusses on recommending resolutions that are standard across for similar issues faced by customers ensuring consistent and better customer experience
    • Efficient and faster troubleshooting process
    • Before NBA integration on an average of three resolutions were tried by agents to figure out successful resolution, which was reduced to two; additionally, NBA provides links of corresponding KB articles to help with corrective actions
    • Reduced number of Incorrect Dispatches
    • The models can also predict the probability of incorrect dispatches consuming the information and provide software resolutions for incorrect Hardware dispatches which constitutes to a reduction of Hardware dispatches by 30%
    • Labeling Dataset using ML Techniques

    Given the use of supervised techniques for this solution, a requirement of labeled data was introduced. The conventional method to get labels for training would have taken more than six months for a team of support agents and subject matter experts to label large datasets for a model to be trained. The ML solution enabled us to extract resolutions and label approximately 80 percent of all cases automatically, with a considerable level of accuracy.