Marketing Analytics

Every marketer is sitting on a pile of campaign data and still guessing. Which channel is actually driving pipeline? Which ad is converting, and which one is just burning budget? Where should I push harder, and what should I kill? These aren't hard questions. They're just hard to answer when your data is spread across six tools and three dashboards none of your team looks at the same way. Dreamdata AI changes that. It pulls your revenue data together and lets you just ask it things. Plain language. Real answers. No pivot tables, no BI ticket, no waiting for someone on the data team to get back to you. What I didn't expect: I never have to leave the tools I'm already in. Dreamdata has an MCP, which means I can pull revenue attribution and campaign performance data directly inside Claude or ChatGPT. I set a reminder every morning to check in and ask what I should be prioritizing that day. It's the first time I've felt like my data is actually working for me instead of the other way around. If you've ever ended a campaign review still unsure what actually drove the results — this is worth a look. Link in the comments. https://dreamdata.io/dreamdata-ai?utm_source=chris-cunningham&utm_medium=Ryan-swindall&utm_campaign=Dreamdata%20AI%20Launch #Dreamdata #B2BMarketing …more

Today's post is 301 words, a 2-minute read. Sources: Generative AI Enterprise — "McKinsey Unveils AI Profit Lessons" 1 big thing: We just entered a new phase of enterprise AI — here's what that means for B2B marketers The question is no longer whether AI works. It is whether your organization can actually deliver on it. Why it matters: McKinsey's 2026 State of AI confirms what most enterprise leaders already sense — AI investment is surging, individual productivity gains are real, but P&L impact remains elusive for most organizations. Driving the news: McKinsey surveyed 1,719 participants across 97 nations. → 80% say AI has improved their individual productivity → Only 37% attribute any EBIT impact to AI — roughly flat year over year despite surging investment Zoom in: Companies breaking through share two practices: they redesign workflows alongside the model rather than layering AI on top of existing ones, and they measure in business outcomes rather than accuracy metrics. → McKinsey anchors this with two case studies — a global manufacturer scaling with AWS and a conglomerate deploying agentic AI at speed Yes, but: 20% of respondents say AI operating costs — tokens, infrastructure, compute — are already constraining usage. Cost management is becoming a real strategic variable, not a footnote. Be smart: If your AI initiatives are measured in time saved or content volume produced, you are measuring the wrong things. Tie AI output to pipeline influence, conversion rate, or content-attributed revenue. That is the only measurement that survives a budget conversation. The bottom line: Technology is rarely the binding constraint. Delivery, adoption, and measurement are. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai #AIStrategy #B2BMarketing …more