Akangsha B.

CRO isn’t one tool it’s a stack working together. Analytics with Google Analytics 4, Heap Analytics, and Mixpanel shows what users do. Behavior tools like Hotjar, Smartlook, and FullStory reveal why they do it. Experimentation platforms such as Optimizely, VWO, and AB Tasty test what works. Add landing pages, AI personalization, and revenue tools like HubSpot and Kameleoon and you turn insights into conversions. CRO Stack = Data + Behavior + Testing + Personalization → Revenue #CRO #ConversionRateOptimization #GA4 #GrowthMarketing #ABTesting #DigitalMarketing #MarketingAnalytics

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No A/B test? No problem. Learn how Regression Discontinuity Design (RDD) can uncover causal effects in observational data by exploiting sharp cutoffs. Dive into the theory and a real-world e-commerce example in Alejandro Alvarez Pérez's newest article.

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We’re proud to partner with Groq to power enterprise AI agents with low-latency inference for performance-critical workloads. Teams on StackAI deploy Groq for latency-sensitive workflows: high-volume document analysis, customer-facing interactions, and internal knowledge retrieval. In each of these situations, fast, reliable responses materially improve user experience and productivity. StackAI and Groq are a prime example of how matching the right inference provider to the right workload helps enterprises build AI agents that perform in real production environments. Want to learn more? Read the full piece at the link in comments.  #StackAI #Groq #EnterpriseAI #AIagents

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Ready to move beyond ML experimentation? Our 6-Phase Implementation Playbook turns field-tested lessons into actionable steps—with specific timelines, deliverables, and success criteria for each stage. Get the complete roadmap from problem definition to production scaling: https://lnkd.in/dxPs9GZ3 #MachineLearning #MLOps #Implementation

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Analytics teams and business users aim for the same outcome: better decisions - but they’re working across different platforms to get there. Analytics teams need the freedom to: - Build analytical pipelines quickly - Maintain data governance as business needs change - Scale themselves to handle the constant stream of ad-hoc requests Business users need: - Faster access to datasets - Confidence they’re looking at the right numbers - Quick analysis so they can make the right decisions Now imagine a single place where analytics teams build and govern data, and business users consume it - quickly, clearly, and consistently. One unified platform acting like a data expert in your pocket. - Scale your teams - Faster access to data - Lower spend

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The voice AI industry has fundamentally misunderstood its own product-market fit. We're obsessing over conversational sophistication while failing at basic speech synthesis. These "performance barriers" Rishav calls out aren't technical limitations; they're the predictable result of prioritizing flashy demos over robust engineering. Companies won't adopt voice agents not because the technology isn't ready, but because vendors have consistently oversold capabilities while underdelivering on reliability. It’s about time someone said it out loud: the barriers to building RELIABLE voice AI (with stats). Here it out 👇

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Building production-ready ML models doesn’t have to be messy. Learn how Julia, GitHub, MLflow & Buildkite come together for scalable, test-driven ML workflows. Featuring JuliaSim’s Mass Experimentation Framework. https://lnkd.in/esWGw8MT #JuliaLang #MachineLearning #CICD #Reproducibility #MLworkflow #ModelValidation #DataScience #MLOps

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You need a data platform built for C2E. Syntasa’s Composable CDP replaces complex data engineering pipelines with a modular Data+AI environment that's built for real-time activation, identity resolution, and predictive decisioning. No more duplicated data. No more hidden compute costs. No more dependency on third-party platforms. Your data, in your cloud, under your control, and built for C2E.

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The Databricks Startups team answers founders’ most common AI questions—where to start without hype, how to stay compliant with sensitive data, what to build versus buy, how to cut hallucinations, and how to keep costs in check. 💡 Get step‑by‑step starting points, governance patterns for sensitive data, a pragmatic build‑versus‑buy lens, tactics to curb hallucinations, and cost controls that scale. 🔗 Full post: https://lnkd.in/ehRa3Yp5 #Databricks #DatabricksForStartups #AI #GenAI #MLOps

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