Where I work, we process about 8 billion messages a year across 20+ brands. That's a lot of customer communication. And for years, we've been tracking all the usual suspects: delivery rates, opt-out percentages, response times, conversion metrics. The standard playbook.
The problem? Those metrics only tell you what happened. They don't tell you how it happened. They miss the texture.
That's where vibe analytics comes in. And no, this isn't just sentiment analysis with a trendy name. Let me explain what we built, why it matters, and why messaging data is uniquely suited to this kind of analysis.
What Vibe Analytics Actually Is
Traditional sentiment analysis is binary or at best ternary: positive, negative, neutral. It's a blunt instrument. Vibe analytics goes deeper. It's about understanding the emotional texture of communication at scale.
Think about the difference between these customer messages:
- "Thanks for the update."
- "Appreciate the quick response!"
- "Finally, thanks."
All three are technically positive. But the vibe? Completely different. The first is neutral-positive. The second is genuinely appreciative. The third is frustrated relief. Standard sentiment analysis would lump them together. Vibe analytics separates them.
We're looking for patterns in tone, urgency, formality, emotional intensity. We're tracking shifts in how customers communicate over time, across different touchpoints, in response to different messaging strategies. We're not just counting words. We're reading the room.
Why Messaging Data Is Perfect for This
There are three reasons messaging data is uniquely suited to vibe analytics:
1. Volume. We're processing billions of messages. That gives us the statistical power to detect subtle shifts in communication patterns that would be invisible in smaller datasets. When you're looking at aggregated trends across thousands of conversations per day, even small changes in tone become significant signals.
2. Signal density. Messages are short-form communication. Every word counts. People don't waste characters in SMS or chat. That means the signal-to-noise ratio is exceptionally high. Unlike email or long-form content where people ramble, messages are distilled communication. Pure signal.
3. Context richness. We know who sent the message, when, in response to what, as part of which conversation thread, for which brand, in which campaign. That contextual metadata makes the vibe data actionable. We're not just seeing that customers are frustrated. We're seeing that customers who received a specific type of notification on Friday evenings are 40% more likely to respond with irritated language.
How We Built It
We rolled this out using Databricks Genie. The technical implementation was deliberately pragmatic. No custom models, no endless fine-tuning. We used Genie's natural language interface to let teams query the vibe data directly without writing SQL.
The architecture is straightforward: messages flow into our data lake, get enriched with vibe metrics via LLM calls (we use embeddings and prompt-based classification), and land in tables that Genie can query. Teams ask questions in plain English like "What's the average frustration score for customers who opted out this month?" and get answers in seconds.
The key technical decision was treating vibe as a first-class dimension, not a post-hoc analysis. We compute vibe scores at ingestion time, not at query time. That means every message in our warehouse has tone, sentiment intensity, and emotional category attached to it, just like it has a timestamp and a sender ID.
We also built feedback loops. When teams spot something interesting in the vibe data, they can flag it. We use those flags to refine our classification prompts and validate that we're capturing the right signals. It's a living system.
What It Enables
Here's where it gets interesting. Vibe analytics surfaces insights that traditional metrics completely miss.
Campaign effectiveness beyond conversion. We had a campaign with decent conversion rates but terrible vibe scores. Customers were converting, but they were annoyed about it. That's a retention time bomb. Traditional metrics said "keep doing this." Vibe analytics said "this is burning goodwill." We adjusted the messaging cadence and saw vibe scores improve without hurting conversion.
Early warning signals. We can now detect frustration building in a customer segment before it shows up in opt-out rates. Think of it like leading vs. lagging indicators. Opt-outs are lagging. Vibe is leading. By the time someone opts out, you've already lost them. But when you see their message tone shifting from neutral to curt over three interactions, you can intervene.
Brand health tracking. We run messaging for 20+ brands. Vibe analytics lets us compare how customers feel about different brands, not just how they behave. Some brands have high engagement but low vibe quality. Others have moderate engagement but strong positive sentiment. That tells you different things about brand positioning and customer relationships.
Tone matching. We can now A/B test message tone, not just content. We've found that matching our message formality to the customer's typical communication style improves response rates. If someone always writes in short, casual messages, formal corporate speak feels alien. Vibe data lets us spot those patterns and personalise accordingly.
Why This Matters Now
Every platform tracks the same KPIs. Open rates, click rates, conversion rates, churn rates. Everyone's optimising for the same numbers. That means those metrics are getting less valuable as competitive differentiators.
Understanding vibe is different. It's not a standard dashboard metric. It requires infrastructure that can handle high-volume text analysis, context-aware interpretation, and natural language querying. Most companies aren't set up for it.
That makes it an edge. When everyone else is optimising delivery rates, you're optimising customer sentiment. When everyone else is pushing for higher open rates, you're making sure customers actually appreciate hearing from you. That compounds.
The best part? The technology to do this is finally accessible. Three years ago, this would have required a team of ML engineers and months of model training. Today, with Databricks Genie and modern LLMs, you can spin up vibe analytics in weeks. The barrier isn't technical anymore. It's conceptual. It's realising that the vibe is the data.
What's Next
We're still early. There's a lot more to explore. Real-time vibe dashboards. Automated tone adjustment based on detected customer mood. Predictive models that forecast relationship quality, not just churn probability.
But the core insight stands: in customer communication, how you make people feel matters as much as what you get them to do. Traditional analytics measures outcomes. Vibe analytics measures relationships. And relationships are what drive long-term value.
If you're sitting on high-volume messaging data and you're not tracking vibe, you're leaving insight on the table. The metrics you're not measuring are the ones your competitors aren't either. That's the opportunity.