B2B ad examples

How many people genuinely visit an asset, and how many are passing through? Christian shows why visit counts become more useful when paired with dwell time, potential bounce rates and competitor comparisons. Watch the full discussion.

When an asset's market changes, the choice is rarely limited to carrying on as usual or starting again. This clip looks at how owners can use evidence about local consumers to make more targeted changes. See the full interview.

First reply time rose, but that doesn’t necessarily mean the team got slower. In this example, an AI assistant uses the Geckoboard Metrics MCP to investigate eight weeks of support data. It finds that part of the increase came from a shift toward email, a slower support channel. After accounting for that change, the remaining delay is concentrated in early-morning Tier 1 email. Breaking the metric down turns a broad concern about team speed into a focused investigation of one channel, tier and time of day.

Claude can investigate why CSAT fell. It shouldn’t have to calculate CSAT from thousands of Zendesk tickets first. But that’s what often happens when an AI assistant only has access to raw records. This week, we launched the Geckoboard Metrics MCP, a connector that gives AI assistants like Claude and ChatGPT access to accurate, real-time, pre-calculated Zendesk metrics. You can ask: “Why did CSAT fall last week?” “Which queue is driving the increase in first response time?” “How does this quarter compare with the previous one?” Geckoboard calculates the metrics. Your AI assistant investigates what changed, combines the numbers with context about your business and creates the report, briefing or resource you asked for. It can also bring in metrics from platforms like Aircall, HubSpot, Pipedrive and Shopify, giving it a broader view of the customer journey. It’s like having a support data analyst inside your AI assistant. The Geckoboard Metrics MCP is now live and is included on every Geckoboard plan. See it in action. Link in comments.

If your work touches export control, defence, or supply chain risk, you need to see how close China really is to fielding its CJ‑1000A. This free report details heat‑resistance challenges, CMC production hurdles, and AECC’s growing contract portfolio, backed by 20 years of project and funding data. Download the full PDF to inform your next briefing or risk review.

𝗥𝗲𝗽𝗼𝗿𝘁: 𝗖𝗵𝗶𝗻𝗮 𝗡𝗲𝗮𝗿𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗼𝗳 𝗙𝗶𝗿𝘀𝘁 𝗖𝗼𝗺𝗺𝗲𝗿𝗰𝗶𝗮𝗹 𝗧𝘂𝗿𝗯𝗼𝗷𝗲𝘁 𝗘𝗻𝗴𝗶𝗻𝗲 China has been doggedly building out its aerospace sector for decades, seeing steady progress but limited breakthroughs. However, the Chinese aerospace sector may be on the verge of completing a long-stand goal: a reliable, advanced turbojet engine. The CJ-1000A, AECC's turbofan for the COMAC C919, is reportedly closing in on certification, signaling confidence in its reliability under extreme conditions. When completed, China will join a select group of countries that can build an advanced commercial jet engine reliable enough for extended use with limited maintenance. Obtaining this technology has been a long road for China. For decades Russia was China’s top choice for licensed jet engine technology. However, China may have reached the limits of this partnership, putting a ceiling on their development until recently. The biggest barrier thus far has been heat. Modern turbines run hotter than most advanced metal alloys can survive, causing the engine to overheat and destroy itself. However, China has been pouring resources into developing Ceramic Matrix Composites (CMCs), alloys able to handle the extreme conditions that would destroy traditional components. To better understand how China achieved this breakthrough, Datenna dug into our information on CMCs and China’s scientific ecosystem: • Research funding into CMC core materials accelerated in the early 2010s, cresting around 2013 and remaining high. This endeavor was backed by NSFC, MOST and provincial funds • The top research contributors are military-affiliated: Harbin Institute of Technology, Northwestern Polytechnical University, and the PLA’s National University of Defense Technology • Over 80% of jet-engine-related CMC patents were filed after 2016, indicating a sharp increase in capabilities and interest • Procurement climbed sharply in the early 2020s. AECC formed the largest cluster in aerospace propulsion and materials, and tendered the biggest contract on record: a $23M CMC research facility in 2022 Although not yet complete, the certification of China’s advanced jet engine would herald a new arrival in a limited and exclusive clique. China’s entrance into other advanced fields has ushered in disruption and adjustment, and if it can complete this new engine, the aerospace field may feel the reverberations for years to come. Read the full report here: https://lnkd.in/e9EA3K-U/?utm_campaign=Report:Hunter-GathererSemiconductor2026%257CImage%252BVideo%257CGlobal&utm_source=linkedin&utm_medium=paid&utm_id=1153103554&hsa_acc=514863577&hsa_cam=1153103554&hsa_grp=839608014&hsa_ad=1573415424&hsa_net=linkedin&hsa_ver=3 #China #Aerospace #OSINT #MCF

Interest and demographic segments reach a lot of people. But is it the right audience? Start with place instead. Pick the stores, venues, and trade areas that matter to your brand, set a time window, and compose the audience. Then send it to your DSP, SSP, or social platform. Bring one brief to a 20-minute call and we'll scope it with you.

A former HSBC financial crime MD on what AI changes in an investigation and what it very much does not. Five reasons fragmented data will break AI investigation agents.

We're excited to welcome Socco Duvall to the Grist Mill team as Director of Data Solutions. In this role, Socco will be leading efforts to design and integrate frontier commercial data infrastructure that helps ensure successful operational outcomes with our mission partners. Socco brings a broad national security background, data fusion expertise, and commercial data experience to help accelerate the delivery of decision-ready authoritative global content. Grist Mill is partnering with government teams to discover, acquire, and operationalize commercial data without rebuilding the underlying infrastructure every time platforms, integrators, or contracts change. “I’m honored to join the Grist Mill team and excited to participate in the transformation of how the United States and our allies anticipate and prepare for emerging security challenges,” said Socco.

Golf brands: an active golfer audience built from course visitation. Restaurants: an audience around your trade area. Retailers: audiences shaped by real-world activity around your stores and your competitors' locations. Local, regional, or national. For brands, a more confident way to plan and buy media. For agencies, a new capability to offer your clients. Bring one brief to a 20-minute call and we'll scope the audience with you.

Eine Studie mit strategischen Erkenntnissen und konkreten Handlungsempfehlungen für Unternehmenslenker, CISOs und Infrastruktur-Verantwortliche. Wo steht Ihre Organisation im Vergleich zu 202 Peers?

The new Coalesce local agentic data engineering experience is now GA! 🎉 About nine months ago, the team started a top-to-bottom rethink of Coalesce with a simple question: how do we build the best agentic SQL editing experience while keeping the core product values our customers love? Well… we've been busy! Today we're announcing new tools that feel familiar (and are fully compatible with your existing Coalesce Projects!), but are built for teams using Claude Code, Cursor, and Cortex Code (CoCo) to build SQL pipelines on Snowflake, Databricks, and BigQuery: - Local editing in the coa CLI—runs off your local files and gives the agent immediate feedback as it makes changes 🚀 - Coalesce Desktop (Mac, Windows, and Linux)—a visual review app that helps you see and understand the AI's changes in real time 🤩 - Native .sql file support—for teams who prefer a SQL-native approach 🔥 - Coalesce MCPs and platform skills 😎 Thank you to our customers for their early feedback, and huge shout out to everyone at Coalesce that made this happen! 🙌 https://lnkd.in/gSjiCPt9

Eine Studie mit strategischen Erkenntnissen und konkreten Handlungsempfehlungen für Unternehmenslenker, CISOs und Infrastruktur-Verantwortliche. Die meisten Unternehmen halten NIS2 für notwendig – bei der Umsetzung klaffen aber deutliche Lücken. Peer-Benchmark aus 202 Organisationen plus 7 priorisierte Maßnahmen für Ihr Board und CISO-Office.

Quand avez-vous vérifié la composition de votre panel pour la dernière fois ? 👀 Les quotas se remplissent. Le problème se loge ailleurs : une catégorie sous-représentée que personne ne voit, jusqu'à ce que votre client conteste l'étude. Inutile d'interroger plus de monde. Il faut corriger la composition de l'échantillon, dès sa construction. Nous avons accompagné plusieurs instituts de sondage dans cette démarche, sans complexifier leur méthodologie terrain. Votre prochain échantillon mérite le même contrôle. ✅ Notre checklist détaille les 9 points à contrôler. Téléchargez-la maintenant.

See you in Vegas at dbt Summit this week! 🎰 No need to gamble on your migration. We're bringing the fastest migrations to dbt Labs. Come find us, grab a fresh coconut, and let's talk migrations. 📍Datafold Booth #311

The Center for Opportunity and Wealth Data provides comprehensive wealth data to inform policy, investment, research, and decision-making. The Local Wealth Explorer covers 50 U.S. cities - including cities across Congressional Black Caucus districts. Explore census-tract-level insights on assets and debt, business ownership, homeownership, and more. Plus 100+ datasets from trusted public and private sources. Create a free account for full access.

The layer where Spark waste shows up is often not the layer where you need to fix it. High compute utilization, long stages, spill, or expensive shuffles are useful signals. But they can easily lead you toward an obvious optimization – and the wrong one. In one case, I was investigating an expensive KNN workload with high compute utilization and a ~10K-line query plan. Runtime cardinality showed a long-running stage spending half its time sorting ~13B intermediate records – most of which were discarded later in the plan. The fix wasn’t compute tuning. It was changing the application logic to reduce the data earlier in the execution. 𝗦𝗼 𝗵𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗴𝗲𝘁 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝘀𝘆𝗺𝗽𝘁𝗼𝗺 𝘁𝗼 𝘁𝗵𝗲 𝗮𝗰𝘁𝘂𝗮𝗹 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻? Trace the execution across infrastructure, Spark runtime, data access, and the physical query plan. Look at executor allocation, task distribution, I/O, shuffle and spill, and operator-level cardinality. The goal is to follow where the work is created, where it accumulates, and where it can actually be eliminated. Roy Daniel and I wrote about this framework and four real production cases in 𝗜𝗻𝘃𝗲𝘀𝘁𝗶𝗴𝗮𝘁𝗶𝗻𝗴 𝗦𝗽𝗮𝗿𝗸 𝗪𝗮𝘀𝘁𝗲 𝗔𝗰𝗿𝗼𝘀𝘀 𝘁𝗵𝗲 𝗦𝘁𝗮𝗰𝗸, now on the Data Engineer Things blog.

Se connecter à NexSIS, c'est un début. Connecter tout son SI, c'est l'objectif. Avec l'échéance NexSIS, beaucoup de SDIS modernisent leurs échanges avec la plateforme nationale. C'est essentiel — mais c'est une première marche. Car un SDIS, ce n'est pas un système isolé : c'est un écosystème d'applications métiers (RH, indemnisation, décisionnel, référentiels agents…) qui doivent, elles aussi, dialoguer. Standardiser les échanges inter-SDIS, c'est bien. Assurer les échanges avec l'ensemble des parties prenantes et briques internes, c'est aller au bout de la logique. C'est précisément le rôle d'un socle d'interopérabilité (ESB) : 🔹 Décloisonner les applications d'un SI souvent hétérogène 🔹 Fiabiliser et transformer les données entre systèmes 🔹 Industrialiser les échanges plutôt que multiplier les traitements manuels 🔹 Préparer une véritable urbanisation, souple et évolutive, pour les usages de demain Les organisations les plus avancées ne se contentent pas de « brancher » une nouvelle plateforme : elles posent les fondations d'un SI durable, où chaque donnée circule au bon endroit, au bon moment. Parce que l'interopérabilité n'est pas une contrainte réglementaire : c'est un investissement dans la résilience. 📍 Retrouvez l'équipe Blueway Software / SoftProject au Mondial Secours Protection 2026 Haute-Savoie Mont-Blanc Alexis de Saint JeanStyven BARRAQUANDRaphaël BARRAT Cyril Blain #RéponseAuxUrgences #SapeursPompiers #SécuritéCivile #Interopérabilité #Urbanisation #OpérationsConnectées

Gen Z targeting strategies based on assumptions are holding you back. Download the free report and find out where the label helps, where it hurts, and five rules to get it right 👇

The Gen Z label your strategy runs on? It might be holding you back. Download the free report and find out where the label helps, where it hurts, and five rules to get it right 👇

Every market researcher sits somewhere on the AI curve. Most don’t know where. Take the 2-minute quiz to find out where your workflow stands.

Replacing customer names with "John Doe" and email addresses with "[email protected]" might feel like you’ve checked the compliance box. But it hasn't. It is what I call lazy masking. And it’s a direct threat to both your data security and your release velocity. When you apply basic, surface-level masking to complex enterprise databases: ❌ Integration tests break because cross-system referential integrity gets destroyed. ❌ QA teams lose days debugging phantom errors that turn out to be data issues. We recently ran a Proof of Concept with an enterprise client, and they uncovered a staggering statistic: 50% of all reported pipeline bugs were not software defects at all. They were caused entirely by bad, inconsistent test data. Under regulations like GDPR and DORA, real compliance isn't just about hiding a name. It requires deterministic masking logic and metadata-driven rules across your entire landscape. Real Test Data Management means achieving 100% compliance without breaking your integration pipelines or slowing down your developers. Curious what a modern, enterprise-ready data masking solution actually requires? I've outlined the 6 non-negotiable features in a blog article. Link in comment.

Build a funnel from events you're already capturing. No upfront instrumentation, historical data from the moment you create it. Then read the stability, performance and feedback issues behind every drop-off step. Ola Diaa builds one live on Oct 6 and walks from "users are dropping here" to "here's what caused it."

Product sees a conversion drop. Engineering sees a healthy app. The user is stuck in between and nobody owns the step where they left. On Oct 6, Ola Diaa shows how Luciq Funnels puts the funnel chart and the crash dashboard in one view: the crashes, ANRs, network failures and user feedback sitting behind each drop-off step. Live, 45 min. Bring a funnel from your own app for the Q&A.