I Asked Claude…
"Show me which marketing channels drive customers with the highest lifetime value, but focus on segments that actually retain past month 12."
That's not a dashboard filter. That's me having a conversation with Claude about e-commerce business data, and getting actionable insights from a BigQuery data warehouse in real-time. Here’s some of the first output with no edits:
Everyone Blames Bad Data. Here's What They're Missing.Â
You need to understand why customer acquisition costs spiked last month. Your dashboard shows the spike, but not the why. You want to dig deeper, maybe it's a specific channel, customer behavior change, or product issue affecting conversion.
So you ask your analytics or marketing team. The "quick question" gets added to their backlog. By the time you get the answer, the problem has either evolved or gotten worse.
Everyone says "the data is the problem", but they're only half right. Raw data is messy, fragmented, and inconsistent. However, the real problem isn't the data itself, it's the semantic layer between your data and your questions. Without proper data modeling and semantic definitions, even the most advanced AI can't give you reliable answers. It doesn't know that "revenue" means "net_revenue_usd" after refunds and discounts, or that "active customer" excludes trial users and dormant accounts.
This is why we've specialized in semantic layer implementations. AI-enabled analytics requires foundation work first: clean, well-modeled data in your warehouse, a semantic layer that defines what your business terms actually mean, and consistent metric definitions across your organization. Once that foundation exists, something remarkable becomes possible.
What if you could just ask your data directly?
Conversational Analytics Changes Everything
Using Credible’s cutting-edge semantic modeling technology, we connected Claude to a BigQuery warehouse using Anthropic's new MCP (Model Context Protocol). The result: instant answers to business questions through natural language conversation.
Traditional Business Intelligence workflow:
Notice metric change in dashboard → Schedule meeting with analytics team → Wait for custom analysis → Get static report → Realize you need different data cuts → Repeat
Conversational analytics:
"Claude, why did our conversion rate drop 15% last week?"
[Seconds later] "The drop correlates with increased social media traffic, which converts 40% lower than email. Email traffic maintained consistent rates. Here's the breakdown..."
"What's the LTV difference between those segments?"
"Should we reallocate budget from social to email?"
Each question builds on the last. Each answer leads to better business decisions.
The Business Impact Is Immediate
Increase Revenue Through Faster Optimization:
Spot underperforming marketing channels and optimization opportunities instantly
Identify cross-sell opportunities and high-value customer segments in real-time
Reduce Costs Through Better Resource Allocation:
Eliminate analytics bottlenecks that slow decision-making by weeks
Free your technical teams from ad-hoc reporting requests to focus on strategic work
Transform How Teams Work:
Marketing and product teams can explore data naturally without SQL knowledge
Leadership can drill into KPIs and ask follow-up questions during live meetings
Why This Matters Now
Your company has incredible data, but accessing insights still feels like pulling teeth. Your analytics stack costs six figures annually. Your dashboards look beautiful.
But when you need to understand why something happened, or what you should do next, you're playing telephone with the data team.
Conversational analytics eliminates this bottleneck entirely.
The Future is Conversational
Dashboards will always have their place for monitoring and visualization. However, when you need to understand why something happened, or explore what if scenarios, conversational analytics changes everything.
This represents a fundamental shift in business intelligence strategy. Instead of building more dashboards, companies will build conversation layers on top of their data. Instead of training teams on BI tools, they'll simply ask questions.
The most successful companies will combine both approaches: dashboards for monitoring, conversations for discovery and decision-making.
Ready to see what conversational analytics could unlock for your business? We're offering free demonstrations for forward-thinking companies to explore this technology with your data and use cases.
Concerned about data privacy and security? Ask us about local models and IT best practices. We’re following the latest research in responsible AI deployment and leading production tools to ensure your data stays protected.