We don't use customers' data (eg data from your data sources) for training purposes. What we use is meta-data like objectives, behavioral data, preferences and feedback loop (was it helpful y/n) to personalize the insights.
It's totally OK to use Narrative BI in a no-UI way via email or slack, but our web UI provides more advanced insights so you may want to check it from time to time. Simply click on an insight in your email and you'll be redirected to a more detailed view.
1. Report automation. People connect their marketing data sources and get automated reports on key metrics
2. Anomaly detection. We generate alerts when we see unusual patterns that affect your key business metrics
3. Natural language insights: we uncover interesting patterns and correlations in your data and provide recommendations on how you can improve/optimize your campaigns
Your interpretation is accurate! The self service connectors we build are mostly marketing/ sales data sources, so the narratives we generate are focused on growth insights and recommendations.
Feel free to try: would love to hear your feedback!
Historically, we've been focused on growth use cases (marketing, sales, PLG). "Data narratives" is how we call our natural language written insights (AI generated). So it might sound like a corporate jargon but actually describes our value proposition. Will think how to make it more clear. Thanks!
Hi HN, Yury and Michael are here to answer your questions.
2 years ago, we launched Narrative BI to make data analytics available for everyone. Since then we helped 2,500+ teams get insights from their marketing data.
We started as a freemium product and built a unique business data set. We used this dataset to launch NBI.AI - a Generative BI platform that can be connected to virtually any structured data source (unlike our previous version designed for specific data connectors).
It looks like we forgot to update the mobile version of the website when we crossed 3000. Thanks for letting us know!
The Snowflake comparison doesn't really make sense here because of different ACVs and businesses models.