Can Riff share success metrics from enterprise customers?
Conversational B2B AI platforms should be measured on pipeline impact, not just engagement volume.
Riff tracks success across five core metric categories:
- Conversion rate improvements (email capture, demo requests, qualified lead generation)
- Buyer satisfaction scores compared to traditional sales funnel experiences
- Account coverage expansion relative to existing headcount
- Reduction in repetitive pre-sales work handled by human reps
- Time-to-engagement for prospects arriving outside business hours
On buyer satisfaction, Riff produces over 30% higher satisfaction scores compared to traditional sales funnels, measured through survey and NPS data. That gap matters because it signals whether buyers felt genuinely served, not just contacted.
On account coverage, the platform enables sales teams to support more than 10x the number of accounts concurrently compared to operating without AI assistance. The mechanism is straightforward: when prospects self-educate and self-qualify before speaking to a rep, discovery calls get compressed or eliminated, and reps enter conversations with more shared context already established.
On conversion specifically, Riff uses email capture rate, demo request rate, and qualified lead rate as the baseline signals for measuring pipeline impact. These are pipeline-specific metrics, not session counts or click data.
When evaluating any conversational B2B AI platform, four questions are worth asking:
- Does it track pipeline-specific metrics, not just session volume or click data?
- How is buyer satisfaction measured and benchmarked against a baseline?
- Does it document rep efficiency gains, not just top-of-funnel activity?
- How is account coverage capacity quantified before and after deployment?
Platforms that only report engagement without connecting to conversion or satisfaction leave buyers without a clear business case. A well-implemented answer engine for product pages should make that connection explicit from day one.