🚀 Case Study: How Finovate Scaled Smarter with Cruxstack
Finovate's growth team already had a strong stack — Mixpanel for analytics, MoEngage for campaigns, and even an LLM-based workflow to detect churn and upsell signals.
But three critical issues kept slowing them down and preventing them from scaling their behavioral intelligence effectively.
The Challenge
1. Ad-hoc insights, no durable traits
Their LLM could answer "who is likely to churn?" but the output was one-off. Turning it into a production-ready attribute required custom pipelines and significant engineering effort.
This meant that every time they wanted to use behavioral insights, they had to rebuild the infrastructure from scratch.
2. Constant re-tuning
Behavior patterns shifted constantly. Engineers had to update prompts, re-index embeddings, and tweak classification logic just to keep signals accurate.
This created a maintenance burden that consumed valuable engineering resources and slowed down product development.
3. From answer to action = engineering bottleneck
Even when the LLM was right, growth teams needed engineers to translate outputs into attributes, sync them into MoEngage, or wire them into the product.
This created a dependency that prevented rapid experimentation and iteration on behavioral strategies.
Enter Cruxstack
Finovate didn't rip out their stack — they layered Cruxstack on top, creating a hybrid approach that gave them the best of both worlds.
Live traits, not one-off outputs
Traits like likely_to_churn, ready_to_upgrade, and power_user were available day one.
This eliminated the need to build custom pipelines and provided immediate access to production-ready behavioral intelligence.
Plug-and-play SDK/API
Every trait showed up as a production-ready label the product and growth teams could act on directly. No more waiting for engineering resources to implement behavioral triggers.
Adaptive ML, zero upkeep
Instead of chasing shifting patterns, Cruxstack tuned thresholds in the background — no prompt wrangling, no constant engineering. The system automatically adapted to changing user behavior.
The Difference
With Cruxstack, Finovate turned insights into impact by eliminating the traditional bottlenecks in behavioral intelligence.
From analysis → action in one step
No more engineering bottlenecks between insights and implementation. Product managers and marketers could now act on behavioral signals immediately without waiting for technical resources.
No engineering maintenance
Traits stay reliable without constant tuning and updates. The system automatically adapts to changing user behavior patterns, freeing up engineering resources for core product development.
Faster experiments
Quick iteration on churn reduction and upsell flows became possible. Teams could test behavioral strategies rapidly and measure impact in real-time.
The Results
The impact of implementing Cruxstack was immediate and measurable across multiple key metrics.
25% churn reduction
Among users flagged at risk. By identifying and proactively engaging users showing churn signals, Finovate was able to retain significantly more customers.
18% lift in upgrades
From ready_to_upgrade users. The ability to identify users ready for expansion led to more successful upsell campaigns and increased revenue per customer.
60% less engineering time
Spent maintaining behavioral pipelines. Engineers could focus on core product features instead of constantly tuning and updating behavioral intelligence infrastructure.
Why It Matters
Finovate's LLM setup was powerful — but hard to operationalize.
Cruxstack bridged the gap, turning patterns into durable, live traits that PMs, marketers, and developers could use instantly, without extra glue work.
From "what's happening?" to "what should we do now?" — all in one step.