Your funnel data lives in four systems that each tell a different story: LinkedIn and Google Ads report clicks, HubSpot reports marketing-qualified leads (MQLs), Salesforce reports pipeline, and BigQuery holds the product usage that explains which signups actually matter. Mad Fish Elements connects Claude, ChatGPT, Microsoft Copilot, or Gemini to all of them - 33 platforms, roughly 1,470 operations - through one governed gateway with per-seat permissions and a full audit trail. We’re Mad Fish Digital, and we built it to end exactly this kind of spreadsheet stitching.
With LinkedIn, Google Ads, HubSpot, and Salesforce behind one gateway, the chasm closes. Ask which campaigns sourced the opportunities that progressed, and the assistant pulls from all four systems in one pass. Cost per pipeline dollar stops being a quarterly science project and becomes the Tuesday-morning number. The QBR argument about join logic ends, because everyone is querying the same connected sources.
Publish the board view once in Data Visualization - spend, funnel conversion, pipeline coverage, product metrics - from the connected platforms, and let scheduled refreshes update the same report in place. Board-deck week becomes board-deck hour: your analyst reviews and annotates instead of rebuilding, and when a director asks for a segment cut, it’s a prompt, not a restart. The link you sent last quarter shows this quarter’s numbers.
Marketers get read access to BigQuery through the same gateway - governed, logged, permission-scoped - so product signals reach campaign decisions on campaign timelines. Which trial cohorts activate? Which accounts’ usage spiked before expansion? Ask, get the answer, and act in LinkedIn or HubSpot in the same thread. The data team keeps control of the warehouse; marketing stops queueing for it.
PLG and sales-led stop being separate stacks. HubSpot, Salesforce, Mailchimp, LinkedIn, Google Ads, Meta, and BigQuery all sit behind the same gateway, so cross-motion questions - product-sourced versus marketing-sourced pipeline, trial-to-paid versus SQL-to-close - are single prompts. Your team reports on the funnel you actually have, not the one your tooling assumed.
Per-seat read, write, and delete permissions, enforced server-side on every call, replace the bottleneck-or-blast-radius choice. Give marketers read access to Salesforce and write access to HubSpot lists; keep destructive operations locked to RevOps. Every action ties to a named user in a full audit trail, so Friday bulk-update mysteries end. The queue shrinks to the requests that genuinely need RevOps judgment.
Marketing reports cost per MQL from LinkedIn and Google Ads. Sales reports pipeline from Salesforce. Between them sits a chasm: which campaigns produced the opportunities that actually closed? Answering that means exporting from three systems, matching records in a spreadsheet, and defending the join logic in every quarterly business review. So the budget conversation runs on cost per lead - the metric everyone agrees is the wrong one - because cost per pipeline dollar is too expensive to compute more than once a quarter.
Every board cycle, the same ritual: pull spend from the ad platforms, funnel conversion from HubSpot, pipeline and bookings from Salesforce, product metrics from BigQuery. Then reconcile the numbers that don’t match, then rebuild the slides. It consumes your best analyst for a week and your CMO for two days of review, and the numbers are stale by the meeting. Worse, ad-hoc board follow-ups - “can we see that cut by segment?” - restart the whole process.
You’re running product-led growth (PLG) and sales-led motions side by side, which means activation, feature adoption, and usage-based expansion signals in BigQuery should be steering marketing: which trial cohorts to nurture, which accounts show buying intent, which campaigns attract users who stick. But the warehouse is the data team’s territory, marketers can’t self-serve it, and by the time a request clears the queue, the campaign it would have informed already shipped.
The PLG motion lives in product analytics and email; the sales-led motion lives in Salesforce, LinkedIn, and outbound. Your marketing team straddles both with tooling that assumes you picked one. Reporting that spans motions - how much pipeline is product-sourced versus marketing-sourced versus sales-sourced - is a manual stitch, and campaign decisions that should weigh both funnels get made inside whichever silo the decision-maker happens to sit in.
List pulls, campaign syncs, field updates, “can you check why these leads didn’t route” - everything flows through a revenue operations (RevOps) queue that’s three weeks deep. Marketers wait. Or worse, they get direct CRM access with broad edit rights and no audit trail, and someone bulk-updates the wrong records on a Friday. The choice between bottleneck and blast radius isn’t a real access model.
Copy these into Claude once you’re connected - swap in your own targets and they’re ready to run.
“Compare LinkedIn spend to SQLs created in HubSpot over the last 90 days, campaign by campaign, and show which audiences bring in SQLs under our $400 cost-per-SQL target.”
The spend-to-SQL answer that usually waits for the QBR - campaign-level, and current.
“Break down this quarter’s Salesforce pipeline by source - paid, organic, product-led, outbound - and compare each source’s average deal size and win rate to last quarter.”
The sourcing picture in one pass - which motion is compounding and which one just looks busy.
“From BigQuery, list paying accounts whose weekly active usage fell more than 30% this month, cross-check renewal dates in Salesforce, and flag any renewing within 90 days with no recent activity logged.”
A churn early-warning list your customer success team can act on - pulled from the warehouse without queueing for the data team.
“Write a three-email re-engagement sequence in HubSpot for trial signups that never activated, one version per signup source, and save them as drafts for my review.”
Segmented copy staged in HubSpot - you approve before anything sends, and the list logic is already done.
“Build a board view - CAC by channel from Google Ads and LinkedIn, pipeline coverage from Salesforce, and net revenue retention from BigQuery - and publish it as a dashboard I can share with our board.”
The standing board link: refreshed on schedule, annotated by your team, and no deck rebuild next cycle.
Boards and revenue teams have seen a thousand dashboards - the ones that land use the chart the question calls for:
Your security team has seen what happens when marketing tools get broad CRM access with shared credentials. Mad Fish Elements takes the opposite approach: every seat authenticates through native OAuth under the user’s own identity - no API keys generated or stored - and read, write, and delete permissions are granted per seat, per platform, enforced server-side on every call. Every operation across every connected system lands in one full audit trail tied to a named user, with automated error monitoring on top. The platform is built around documented security controls, so the vendor-review checklist starts with documentation instead of exceptions.
A higher base buys a lower rate. Pay more up front and keep more of your spend. Every Ad Ops tier includes governed platform access, per-user grants, and administrator controls.
See what's wrong across every account.
Find issues and fix them — full read/write automation.
Everything in Reporting, plus:
Proactive optimization across every brand you own.
Everything in Standard, plus:
Your playbooks, your rules, at scale.
Everything in Premium, plus:
How managed spend is measured: total spend on connected ad accounts during the billing month, measured from the platforms' own metrics APIs at billing time, regardless of where changes originated. Exact accounts and queries are visible in the audit trail. Disconnected accounts are excluded.
Data Visualization is tenant-wide: Viz Starter /mo for up to 50 saved reports, Viz Team 9/mo for up to 100 reports (included with Ad Ops Premium), or Viz Scale 9/mo for unlimited reports.
No - it connects what you already have. HubSpot, Salesforce, LinkedIn, Google Ads, GA4, and BigQuery stay your systems of record; the gateway lets your team query and act across them in one conversation, and Data Visualization publishes governed reports from that connected data.
Yes. Read, write, and delete are separate per-platform grants on each seat, enforced server-side on every call. A marketer can have read-only Salesforce alongside read-write HubSpot - and no request phrased to the AI can exceed what the seat allows.
BigQuery is one of the 33 connected platforms, so a seat with read access can ask questions in natural language through their assistant - Claude, ChatGPT, Microsoft Copilot, or Gemini - with every query logged. The data team governs the grant; marketers stop queueing for routine pulls.
Neither. The gateway spans CRM (HubSpot, Salesforce), ads (LinkedIn, Google Ads, Meta), email (Mailchimp), analytics (GA4, BigQuery), and web (WordPress, Unbounce, Instapage, Landingi), so both motions run through the same governed layer and cross-motion reporting is native rather than stitched.
Every operation through the gateway - reads, writes, deletes, across every platform - tied to the individual user’s OAuth identity, with automated error monitoring flagging failures. When someone asks who changed a campaign budget or updated a lead list, the answer is a lookup.
The numbers your board wants already exist in your stack; they’re just spread across four logins and a spreadsheet. Mad Fish Elements connects them behind one governed layer so your team asks questions instead of assembling exports. Bring your funnel to a walkthrough and watch it get answered live.
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