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30 posts about Analytics. Every summary links to the original.
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A/B testing trades traffic, time, and exposure risk for evidence. This explainer maps the benefit of a controlled comparison, the real cost of running one, and the condition that decides when measuring beats shipping on judgment.
Compare Brandwatch's AI speed features—Ask Iris, Query Writer, and AI summaries—with the governed checks that make social insights trustworthy before teams act.
Anomaly detection identifies meaningful departures from expected behavior. This explainer shows how signals, baselines, context, and alert routing turn unusual measurements into engineer-ready findings.
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Intercom launched a suite of new features including Custom Reports, Conversation Topics, and Macros to help teams balance personal and efficient customer communication at scale. Custom Reports lets teams tailor dashboards in just a few simple steps by building custom charts from user and conversation data attributes and exporting them. Conversation Topics provides a machine-learning powered engine that visually represents what customers are talking about and how often, giving teams an at-a-glance view of top concerns. Macros automate common inbox actions like tagging, assigning, snoozing, and closing conversations with a single click, boosting efficiency by up to 43%. Additional updates include Series targeting and split testing, enhanced Articles, mobile app tagging, permissions, and custom return paths for email deliverability.
Customer support teams interact with customers daily and possess valuable product and customer insights that product teams often overlook. Treating support as an outsourced function or ignoring their conversations wastes unsolicited feedback that differs from survey data. Intercom tags every support conversation with team and category tags to make feedback actionable, enabling product teams to create dashboards, spot trends, and explore conversations for insights. For example, tagging ‘product confusion’ revealed an onboarding issue where customers saw nothing if they only completed one of two setup steps, leading to a pop-up that told customers exactly what they needed to do and reduced those conversations. Product teams also use tags like ‘unaware’ to improve feature discoverability, track bug reports via a GitHub integration, and compile quarterly ‘customer voice reports’ of top feature requests while closing the feedback loop with customers.
Klaus 2022 Customer Service Quality Benchmark Report, based on a survey of 300 support leaders, identifies three key trends. First, support teams are measuring impact more than ever: average CSAT rose 14% to 89%, and average NPS increased from 45 to 51; 10% more teams track CSAT, and NPS tracking jumped from 28% to 48%. Second, maintaining quality at scale is the top challenge, with 40% citing reducing repetitive queries; teams use first-party data, chatbots, and automation to deliver personal, fast support while scaling. Third, proactive and self-serve support are growing priorities: 70% plan to invest more in proactive support, and 73% found self-service reduced workload; knowledge bases compound in value over time. The report recommends focusing on the right metrics, collaborating with sales and marketing, and continuously learning and optimizing to elevate support’s business role.
The NVIDIA KGMON team placed second in the KDD Cup 2026 Data Agents competition by building a reliable agent system around a small, fixed LLM. The system constrained the agent’s action space through a harness that normalized data access by converting CSV and JSON files into SQLite tables, exposed a limited set of custom functions for schema inspection, SQL queries, and answer writing, and seeded the agent with schema context before analysis. Documents and videos were handled through targeted tools like prose_helper for extracting rules from documents and keyframe extraction with aligned transcripts for video evidence, keeping the main LLM context focused. The team emphasized inspectability by recording execution traces and running repeated attempts, using evaluation and trajectory review to identify failures and refine the harness. The key lesson is that reliability for data analytics agents often comes from the harness design, not the model’s capabilities.
Customer service metrics must evolve as AI chatbots become the first point of contact for support teams. Traditional metrics like first response time and average handle time now need separate reporting for bot and human interactions, since AI delivers near-instant responses and handles simpler queries. Cases handled loses relevance as support reps focus on more complex issues and tasks such as help center content creation; automated resolution rate becomes a key productivity measure. New emerging metrics include bot involvement rate, bot engagement rate, and AI-powered conversational insights that analyze customer interactions at scale. Leaders should reset SLAs and benchmarks while tracking both bot and human performance to get a true measure of success, as 69% of support leaders plan to increase AI investment.
Cloudflare AI Gateway’s User Insights now provides context beyond model names and request counts, using a dedicated Worker to classify conversations by task type such as coding, research, summarization, or formatting. The “model overkill” view identifies conversations where a high-capability reasoning model is used for simple tasks, showing which users or agents are driving that usage and the associated cost. Teams can analyze conversation turns to see how many back-and-forth exchanges a task requires, helping uncover inefficiencies in prompts or workflows. The Potential Savings view and Auto Router (closed beta) use task and model-fit signals to automatically route requests to appropriate models without a separate rule for each workload. These features are free for AI Gateway users and require stable user_id and session_id for analysis, with optional Access integration.
Brandwatch introduced seven AI-powered updates to help analysts, marketers, and social media managers keep pace with fast-moving online conversations. Iris AI features like Query Writer and Query Fixer streamline building comprehensive research queries by allowing a user to describe a topic through chat. The Ask Iris AI chat on the homepage and dashboards delivers instant summaries and topic breakdowns without requiring a pre-built query. Enhanced Alert and Signal emails now include AI-generated summaries so users can assess breaking stories or issues without reading individual mentions. Other updates include AI-generated categories for segmenting datasets, industry benchmarks for social performance, semantic search for YouTube creator discovery, and automated message classifiers in Engage.
Cloudflare introduced new post-quantum cryptography visibility tools in its Application Security and Logs products, including Logpush, Log Explorer, and the HTTP Traffic Analytics dashboard, to help customers monitor encryption adoption. These tools surface the key exchange algorithm negotiated on every incoming request, enabling customers to audit their post-quantum posture, assess compliance, and identify cryptographic gaps across their domains. The primary post-quantum algorithm in TLS 1.3 is X25519MLKEM768, a hybrid of classical ECDHE over curve X25519 and the post-quantum ML-KEM, which provides belt-and-suspenders security against harvest-now-decrypt-later attacks. Cloudflare Radar data shows about 70% of browser-generated traffic uses post-quantum encryption on the visitor-to-Cloudflare connection, but only 15% of origin connections to Cloudflare use hybrid ML-KEM. For legacy origin servers that cannot support modern post-quantum cryptography, Cloudflare Tunnel can tunnel traffic over TLS 1.3 with X25519MLKEM768 without requiring any upgrade to the origin server itself.
The ClickHouse workload for Microsoft Fabric brings ClickHouse as a native acceleration layer for OneLake, now in public preview. It provisions dedicated ClickHouse Cloud services on Azure via the Fabric Extensibility Toolkit, using a ClickHouse item in the workspace. Users sync OneLake tables through ClickPipes as point-in-time snapshots with no ingestion charges. The embedded SQL console and external tools (Power BI, Copilot Studio, GitHub Copilot) can then query billions of rows with sub-second response times at high concurrency. Planned future features include write-back to OneLake, continuous sync, permission sync, and deeper Power BI integration.
Cloudflare has released the BEACON dataset, an anonymized collection of billions of real-user performance measurements from 10,000 of the largest websites. The dataset reports Core Web Vitals (LCP, CLS, INP) with full histograms, enabling analysis of any percentile including the long tail. It reveals that WebKit browsers underperform Blink in 46 countries, and that resource-discovery and render-blocking contribute more to slow LCP than download time. Soft navigations in single-page applications load two to three times faster than hard navigations, but landing pages remain heavy. The dataset is publicly available on Google BigQuery and includes differential privacy via the RAPPOR algorithm to protect individual user data.
Databricks says it was recognized as a Customers’ Choice in Gartner Peer Insights’ Voice of the Customer for Analytics and Business Intelligence Platforms, based on verified reviews from customers using platforms in production. The post reports a 4.8 out of 5 overall rating and 94% willingness to recommend, while noting that the designation reflects customer adoption and overall experience meeting or exceeding market averages. It presents Databricks AI/BI, AI/BI Dashboards, Genie Spaces, and Genie as a unified architecture combining governed live data, AI, business intelligence, and Unity Catalog controls without moving or duplicating datasets. The described use cases include conversational exploration, dashboard and visualization development, consolidating fragmented reporting, and expanding governed analytics access, with the company positioning these capabilities as a foundation for agentic analytics.
Agentic analytics is an approach in which autonomous AI agents continuously monitor data, detect patterns and anomalies, generate contextual insights, and take actions with minimal human intervention. Unlike traditional business intelligence, which relies on people to query dashboards and interpret reports, these systems operate as goal-directed decision loops that can recommend or trigger alerts, forecasts, workflows, and system changes within defined boundaries. Their architecture combines data access, agent orchestration, LLM-based reasoning, a semantic layer, action integrations, and feedback signals. The source identifies faster insight, lower reporting overhead, reduced analytics backlogs, and potentially better forecasting as benefits, while stressing governance, auditability, security, monitoring, and human oversight to control errors and unintended outcomes.
Agentic analytics increases the need for trustworthy semantics and governance because AI systems must interpret business definitions, trace outputs, and support follow-up questions beyond static dashboards. Nick Eayrs argues that fragmented metrics across BI tools, proprietary semantic layers, and duplicated pipelines erode trust, slow decisions, and increase engineering cost. The proposed foundation is an open, interoperable semantic layer where certified metrics such as revenue, churn, and customer lifetime value are explicitly defined, reusable, and accessible through SQL to BI, AI interfaces, notebooks, and agents. Organizations should establish governed data foundations and a catalog first, certify metrics next, then add AI and evaluate its outputs. Examples include NTT Docomo reducing manual log-analysis time from 66 hours to 6 hours and Net One Systems cutting support response time by 75 percent.
Brandwatch describes an all-in-one social media suite that combines AI, analytics, channel management, consumer intelligence, and specialized tools for enterprise teams. Its consumer-intelligence offering provides access to the world’s largest archive of consumer opinion and uses proprietary and generative AI to identify emerging trends, while threat monitoring covers more than 100 million sources with real-time alerts. A collaborative content calendar supports channels, teams, workflows, approvals, and campaigns, and a unified social inbox handles community management and customer support across networks. The stated goal is to help organizations make better decisions, align content with their brands, respond quickly to threats, and produce measurable results and return on investment; the page also cites recognition by industry analysts and customer trust.
Campaign post-mortems often report reach, engagement, impressions, and sentiment without establishing whether a campaign changed perception or behavior. It argues that social listening should measure sentiment, themes, mention volume, and audience conversation shifts, while search intelligence measures category and branded demand, promoted benefits, and share of search. A client example contrasts a campaign that generated strong social buzz while category search declined with a later campaign that reversed declining demand, captured two-thirds of share of search, and grew search intent 134%. The difference was attributed to combining socially engaging creative with media strategy and messaging designed to drive search demand. The proposed framework aligns measurement with campaign goals and uses gaps between social sentiment and search intent to identify awareness without demand, existing intent without audience expansion, or stronger market impact.
An analysis of footwear conversations around the 2026 World Cup compares how sponsorship, search demand, social attention, and sentiment shaped the competition between Adidas, Nike, Puma, New Balance, and other brands. Consumer Research tracked tournament-related mentions from June 11 to July 19 and paired them with search queries across the US, UK, and Canada. Adidas led tournament-specific footwear conversation with 63% of mentions, while Nike led broader brand discussion with 995k mentions, brand search momentum, and 78% positive sentiment. Nike’s cultural activations, including LISA’s “Rip The Script” campaign and Ronaldo’s Mercurial moments, generated shareable attention, whereas Adidas benefited from official sponsorship, the Trionda match ball, and player storytelling. Pink cleats became a cross-brand liability, attracting more than 5.5k mentions with nearly 70% negative sentiment, showing that visibility did not automatically produce favorable attention.
This analysis examines how five non-sponsor brands used football-related moments to gain attention without buying official tournament partnerships. Consumer Research tracked brand mentions in public social and news content from June 11 to July 8, identifying responses to clean-stadium rules, fan travel, beer shortages, and rising ranch interest. Levi’s covered-logo campaign generated 20.1k mentions, while Heinz’s taped condiment packaging produced 5.1k; Miller Lite and Delta reacted to the Tartan Army with a beer barge and airport welcome. Kraft followed a TSA post with a limited-edition “TSA-Compliant Ranch” kit, recording over 4k mentions and a 3,998% increase over the previous timeframe. The examples support a model based on speed, authenticity, and real-time listening, with social and search signals helping brands act while trends are building.
Resend announces Matt Gilbert as a Data Analyst on its Trust & Safety team. He spent 10 years as a Deliverability Engineer at Mailchimp, handling Delivery-team data and maintaining sending infrastructure that pushed around a billion emails a day, after earlier work on the Mandrill transactional mail service. Gilbert describes email delivery as a multidisciplinary field spanning MTA operations, data engineering, analytics, mail-queue performance, and troubleshooting delivery and inbox-placement problems, which led him to specialize in data engineering and analytics. He says he joined Resend because email remains an open, globally available way for services to communicate, and plans to help build data-driven capabilities and intelligence that enable customers to send better and reach inboxes quickly.
Consumer Research tracked close to 32.6 million tournament-related mentions from June 10 to June 28 to examine how fans experienced the tournament through food, beverages, celebrations, and public events. International visitors reacted strongly to American staples: ranch dressing reached 70.3k mentions, free refills 45k, free or unlimited bread more than 30k, and barbecue nearly 64k, while Google interest in “ranch dressing” rose 586.4% and “ranch dressing ingredients” 600%. The experience also exposed friction, with stadium beers costing more than $20 and hydration breaks drawing negative sentiment in over 79% of sentiment-categorized mentions. Watch parties generated over 226k mentions, and New York held 22% of host-city watch-party conversation, offering alternatives to stadium prices. Norway’s “Viking Row” reached nearly 229k mentions, showing how organic fan rituals can spread across borders and create fast-moving opportunities for brands.
AI search is reshaping consumer discovery, but the post says most brands do not know what AI platforms say about them. It presents a four-step assessment: choose one country, define a focused category, identify roughly 100 consumer questions, and test whether the brand appears in answers from ChatGPT, Gemini, Perplexity, and Google AI Overviews. Search Intelligence platforms track queries, mentions, recommendations, citations, and content gaps across search and AI tools. The post recommends answering real queries with dedicated content, earning coverage from accessible independent sources, participating helpfully in online conversations, and monitoring AI answers monthly because they change. Its examples report a 25% reduction in Orange call-center contacts, 50% lower Campari costs, and Aromazon reaching €40 million in annual revenue.
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