What metrics would you use to evaluate [24]7.ai's Active Share co-browsing solution?

Introduction

Evaluating [24]7.ai's Active Share co-browsing solution requires a comprehensive approach to product success metrics. To address this challenge effectively, I'll follow a structured framework covering core metrics, supporting indicators, and risk factors while considering all key stakeholders.

Step 1

Product Context

[24]7.ai's Active Share is a co-browsing solution that enables customer service agents to view and interact with a customer's web browser in real-time. This technology allows agents to provide more efficient and personalized support by directly assisting customers with online tasks.

Key stakeholders include:

1. Customers seeking support
2. Customer service agents
3. IT and security teams
4. Business leadership

The user flow typically involves:

1. Customer initiates a support request
2. Agent sends a co-browsing invitation
3. Customer accepts, granting temporary access
4. Agent views and interacts with the customer's screen
5. Session ends, terminating access

Active Share fits into [24]7.ai's broader strategy of enhancing customer experience through AI-powered solutions. It complements their chatbot and voice AI offerings, providing a more hands-on support option when needed.

Compared to competitors like Glance and Surfly, Active Share emphasizes security and ease of integration with existing customer service platforms.

Product Lifecycle Stage: Active Share is likely in the growth stage, with increasing adoption among enterprises seeking to improve their customer support capabilities.

Software-specific context:

• Platform: Web-based, compatible with major browsers
• Integration: APIs for CRM and ticketing systems
• Deployment: Cloud-based SaaS model