Every user doesn’t behave the same way, and treating them like they do is a missed opportunity. Data segmentation helps product and analytics teams go beyond surface-level metrics by comparing meaningful user cohorts. Instead of simply tracking engagement or conversion at the aggregate level, you can understand how behavior differs across power users, churned customers, test variants, and more.
A 2023 survey by McKinsey found that companies using advanced cohort analytics are 2.3x more likely to outperform peers on growth KPIs. The insight? It’s not just about data volume: it’s about data value.
The Challenge with Manual Cohorting
Traditionally, building segments requires SQL expertise or hours of setup time in rigid tools. It often falls on analysts or engineers, leaving product managers dependent on ticket queues.
That’s where natural language processing (NLP) comes in. With solutions like Kubit Lumos AI, teams can describe the user group they want, like “users who added to cart but didn’t purchase last week”, and the system automatically generates the segment.
This blend of NLP analytics and warehouse-native access means teams can:
- Move faster without sacrificing accuracy
- Ask better questions and explore hypotheses instantly
- Democratize access to behavioral analysis across teams
According to Gartner, 70% of organizations will adopt NLP-powered analytics tools by 2026, up from just 25% in 2023.
What You Can Do With Segmented Insights
Instead of relying on averages or all-users reports, segmentation allows for highly contextual analysis. Here’s what teams can uncover:
- Which behaviors signal long-term retention
- Which variant of a feature drives more engagement
- Where casual users drop off vs. power users who convert
- What triggers churn across specific user types
When you understand these deltas, you can design more targeted experiments, personalize product experiences, and proactively prevent drop-off.
The Role of Natural Language Processing
Manually writing SQL, syncing tools, and defining filters across disconnected platforms made it difficult for non-technical stakeholders to explore behavioral questions on their own. Even when data was technically available, it wasn’t always accessible.
Natural language processing (NLP) is changing that. By translating plain English into structured queries, NLP functionality now makes user segmentation as simple as typing into a search bar.
Instead of logging a request with the data team, a product manager can simply write:
“Show me users who signed up in the last 30 days but haven’t completed onboarding” —and get an actionable cohort in seconds.
Tools like Kubit’s Lumos AI combine NLP with direct data warehouse access, enabling precise, up-to-date cohort generation without duplication or delay. This empowers teams to:
- Run A/B tests faster with on-the-fly group creation
- Compare funnel performance across different audience segments
- Identify drop-off patterns, usage trends, and activation milestones in real time
Make Segmentation Core to Your Workflow
Whether you’re optimizing onboarding, reducing churn, or scaling a new feature, data segmentation is no longer optional. It’s a prerequisite for understanding your users with clarity.
By combining granular comparisons with the accessibility of natural language processing, teams gain speed, accuracy, and independence—three must-haves for modern product success.
Because understanding product performance isn’t just about what happened—it’s about who it happened to, and why.
