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What Changes When You Start Using AI To Interrogate Data

card #1

Campaign Analysis — From Guesswork to Clarity

Case study - Revolutionizing Event ROI Image

The Challenge:

A tech marketing team was spending days preparing for every QBR but still found it hard to explain which activities were truly driving results. Multiple dashboards told different versions of the truth, and leadership often questioned the numbers.

What We Did:

We uploaded their Q3 performance data into a simple AI workflow and taught the team to ask direct questions like, "Which channels are actually driving pipeline?" and "Which campaigns generated opportunities that closed?"

Result

Board prep dropped from two days to half a day. The team walked into their next QBR with a more coherent story — not flawless, but far clearer and easier to defend.

card #2

Campaign Analysis — From Guesswork to Clarity

Case study - Building Out The MarTech Stack

The Challenge:

A tech marketing team was spending days preparing for every QBR but still found it hard to explain which activities were truly driving results. Multiple dashboards told different versions of the truth, and leadership often questioned the numbers.

What We Did:

We uploaded their Q3 performance data into a simple AI workflow and taught the team to ask direct questions like, "Which channels are actually driving pipeline?" and "Which campaigns generated opportunities that closed?"

Result

Board prep dropped from two days to half a day. The team walked into their next QBR with a more coherent story — not flawless, but far clearer and easier to defend.

card #3

Before & After — Attribution Clarity

The Challenge:

A marketing ops leader was stuck in endless debates about attribution. LinkedIn said one thing. HubSpot said another. Finance didn't trust either. Every meeting turned into an argument instead of a decision.

What We Did:

We didn't try to fix attribution (that's a bigger project). Instead, we taught the team to use AI to surface patterns across all their data sources — so they could say, "Here's what all the data agrees on, and here's where we need to investigate further."

Result

The arguments stopped. Decisions got made. And the team finally had a shared language for talking about what was working.