Google, Meta and LinkedIn ads on one dashboard: spend, CPC, CTR and conversions on a single screen

What Google calls a “conversion” is not what Meta calls a “result”, and LinkedIn counts even clicks differently. Building the weekly report by exporting from each platform and aligning the columns eats half of Monday morning in most small teams.

The Tecrube panel does that alignment for you. Spend, clicks, CPC, CTR and conversions for every connected ad account appear in one table over one date range. You do not set up an extra tool or a data warehouse to normalise the platform differences.

The panel's main view is a plain table of platform rows and metric columns. If a software company collects lead forms on LinkedIn and demo sign-ups on Google, both goals sit side by side in the same conversion column; comparing cost per conversion needs no arithmetic.

Change the date range and every platform updates together. The comparison with the previous period shows as a percentage next to each cell.

The table tells you about today; the trend chart tells you about direction. Seeing spend and conversions on the same axis exposes the week when budget went up while conversions stood still.

The funnel view separates the drop from impression to click and from click to conversion per platform. Plenty of clicks on Meta but few conversions points at the landing page; plenty of impressions on Google but few clicks points at the ad copy. The funnel tells you which floor to inspect.

Browsing the panel, filtering the table or studying the trend never spends a credit. Credits go only to actions you request in chat. A free account comes with 75 credits that never expire; Pro includes 5,000 credits a month and Agency is unlimited.

When you spot a problem in the panel you request the change from your chat in Claude or ChatGPT: “lower the LinkedIn campaign's daily budget to $40”. The assistant shows a preview, you approve, and the panel reflects the new value within seconds.

First, add the connector to your assistant and link your ad accounts with OAuth; no password is shared. Second, get to know the account with read operations in the panel and in chat: which campaign spends what, which audience converts. Third, apply changes with preview and approval.

The routine is identical for a one-person ecommerce shop and for an agency running ten clients; on the Agency plan only the number of accounts and the scope of the audit log grow.

Business owners who run their own ads and are tired of opening three dashboards every morning; agencies that want the client report ready in minutes; companies that want marketing and technical teams looking at the same numbers.

Data is read only from the accounts you connected, is never shared with third parties, and the connection can be removed from the panel in one click.

The panel shows what changed; the chat finds out why. Say the panel shows LinkedIn's cost per conversion clearly up on last week. You write in chat: “why did CPA rise on LinkedIn this week? Break it down by campaign, audience and ad.” The assistant reads the same live data in more detail and tells you which audience or ad the increase came from.

Used together, the two turn reporting from reading a table into making a diagnosis. You spot the row that deviates most across four platforms in the panel, ask in chat for the reason behind it, and apply the fix in the same chat with preview and approval.

The same flow works for the weekly client or management report: instead of panel screenshots you ask in chat for “this week's platform comparison with a three-point commentary”. The annotated summary arrives in seconds and, being a read, spends no credits.

A side effect of using the panel and chat together is better communication inside the team. The marketing lead takes a deviation spotted in the panel into chat and forwards the cause the assistant found, along with the suggested change, to a colleague via a link to the same conversation. The “which number are we looking at” debate disappears; everyone sees the same live data, the same date range and the same conversion definition. Because every approved change is written to the audit log, who did what and why is never in dispute later.