CaliberMind

We're excited to announce the launch of the CaliberMind MCP Server! ✨ This open MCP integration connects Claude, ChatGPT or any other MCP-compatible AI assistant directly to CaliberMind's governed unified marketing data platform with access to multi-touch attribution, buyer journeys, marketing mix modeling, and funnels. This launch enables customers to better leverage their AI investments, using fewer tokens to get accurate answers from LLMs. The CaliberMind MCP Server acts as a secure, governed query layer with schema context, table relationships, and business logic pre-loaded so AI can query it accurately without any data prep. It provides: 🔌 Pre-Built Data Pipelines: Instant connection to 170+ marketing data sources, fully unified and standardized. 📝 Governed Schema & Context: Field-level descriptions and table relationships mean Claude understands what the data represents before running a query. 📊 Advanced Analytics Access: AI can directly query complex data domains including multi-touch attribution, funnel velocity, buyer journeys, account-level scoring, and marketing mix modeling. 💸 Token-Efficient Architecture: Instead of flooding an LLM's context window with raw, unstructured data, CaliberMind provides a lean semantic schema  reducing token costs while returning accurate answers that reconcile with core business systems. Learn more about the CaliberMind MCP Server: https://hubs.ly/Q04lVKRW0

Every marketing team has attempted to do more with less... few actually succeed. Superside is one of those few... want to know how they did it? Join #DemandDay live tomorrow to see how they grew enterprise pipeline 30% while lowering cost by 31% with smarter spend allocation. 💸 You'll learn how they: ↳ Built a "marketing analytics" function on top of unified marketing data using CaliberMind. ↳ See how channel investments were driving real results downfunnel. ↳ Cut spend on less efficient channels, and increase budget on winners. When: Thursday 5/18 @ 12:10 – 12:30 PM PST Featured Speakers: Cassandra Gill | Sr. Director of Growth Joanna Eliopoulos | Sr. Marketing Operationss & Analytics Manager Michelle J. | Head of Marketing Analytics & CRO Register to join to catch this and up to 7 other all-star sessions put together by Eric Linssen: https://hubs.ly/Q04lsFn70

Next time on the ʙᴇꜱᴛ ᴅᴇᴍᴀɴᴅ ᴄᴀᴍᴘᴀɪɢɴ 🏆 award-winning Hitchhiker's Guide to Marketing Analytics series... MCP Servers 101! Join us live 𝙩𝙤𝙢𝙤𝙧𝙧𝙤𝙬 at 1 PM ET to learn how MCPs (Model Context Protocol) fit into your broader marketing analytics strategy. You'll learn from experts across marketing, data science, and engineering We'll cover: ↳ What an MCP server is and how it works ↳ How MCP acts as the bridge between your data warehouse and tools like Claude or ChatGPT ↳ What real-time, AI-powered reporting requires under the hood ↳The do's and don'ts of moving your marketing analytics to LLMs Featured Speakers: Nadia Davis, MBA, Joe Schattschneider, Nolan Garrido 𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐭𝐨 𝐉𝐨𝐢𝐧 ➡️: https://hubs.ly/Q04lsk2j0

This one really hit home... 🥹 Badges are great, but what really resonates is when our clients share feedback like this. 💚 Check out more of our reviews on G2 and see why enterprise marketers choose CaliberMind to make better decisions with their data. More on G2: https://hubs.ly/Q04lrNyY0

Hot take: the hardest part of Marketing Mix Modeling has nothing to do with the model. It's the data. 🔢 In B2B, you're dealing with: ↳ CRM data maintained for Sales Ops, not analytics — deals logged late, stages used inconsistently, revenue living in a different system ↳ Channel spend arriving at incompatible granularities from 6+ platforms, some without clean spend data at all ↳ Missing weeks, inconsistent taxonomy, and business events sitting in someone's memory instead of a structured data layer The model is downstream of the data. Every shortcut at the data layer shows up as noise in the output — wider credible intervals, convergence failures, budget recommendations that don't pass the smell test. That foundation — unified go-to-market data, standardized channel taxonomy, time-series-ready revenue data — is exactly what CaliberMind is built to deliver. What would take a multi-month engineering effort becomes an analytical exercise measured in days. Check out our full writeup on the data problem with MMM here: https://hubs.ly/Q04l59Kr0

Cassandra (ex-growth mktg, Datadog) got recruited to Superside last year to hit "aggressively higher goals" without more budget. She actually did it. She's sharing how in the live virtual summit we're hosting on June 18 (check it out: https://luma.com/irne4d20). If you're running a scaled DG machine, or taking a new role soon, I think you'll find this super interesting. She didn't do any crazy hacks. No AI, just the fundamentals taken extremely seriously: -- She and her team fixed their data and attribution. Built a "marketing analytics" function on top of unified marketing data using CaliberMind. -- So they could clearly see how channel investments were driving real results downfunnel. -- So they could cut spend on less efficient channels, and increase budget on winners. Simple. But not easy. This takes extreme discipline and confidence to invest in getting this data infrastructure right, and actually follow through. Cassandra had both of those because she was running growth marketing inside one of the most disciplined and efficient GTM machines in tech, Datadog. And now she's sharing it with us. For free. On June 18th. See you there!

One last Gartner Symposium takeaway, and it's a doozy... The #1 challenge of tech CMOs is still to prove ROI... But there's a Catch-22: When a marketing team's effectiveness is doubted, the budget they're given is thinner. At which point, their lack of budget becomes an additional hurdle to tackling the ROI challenge. So to whoever needs to hear this, invest in your data and analytics foundation. 💪 You'll thank yourself later.

Trying to vibe code attribution and oop... looks like you're out of tokens 🙊 Sam Altman just said AI costs went from something nobody mentioned at the start of 2026 to one of the top complaints he hears from enterprise customers. Companies are burning through annual AI budgets in a single quarter. For B2B marketing and ops teams, this hits close to home. Attribution is exactly the kind of workflow where it's tempting to throw AI at everything... and where that backfires fastest. Here are a few principles we've learned from experience: 1️⃣ Get your data AI-ready first. Think through every step you take with your data — routine cleaning, normalization, unification — and have those done before you involve AI. A natural language interface on top of fragmented, siloed data doesn't give you insight. It gives you confident-sounding answers built on bad inputs. Garbage in, garbage out, but now at scale and with a monthly invoice. 2️⃣ Use LLMs for what they're actually good at. Summaries. Open-ended generation. Making sense of unstructured data. If the data is structured and the output you need is rule-based and concrete like attribution logic, funnel math, or pipeline reporting — deterministic software is more predictable, more auditable, and a lot cheaper. Save the token budget for where the models excel. 3️⃣ Know when not to use AI. The best ROI on AI isn't using it everywhere. It's knowing which 20% of tasks actually benefit from it. If you're 'tokenmaxxing' your way through attribution queries that a SQL rule could answer in milliseconds, you're burning budget. The companies winning with AI right now aren't the ones using it the most. They're the ones using it responsibly. Interested in more nuanced discussion around AI for marketing analytics? Join us next week for MCP Servers 101 on Hitchhiker's Guide to Marketing Analytics: https://hubs.ly/Q04kN_d60