B2B SaaS: +28% conversion to demo requests

The problem

CRM data and advertising data lived in separate systems with different definitions. Marketing could report spend and sales could report deals, but nobody could forecast leads or say which campaigns produced the demo requests that closed.

What we did

One reporting surface fed by both systems, AI scoring on inbound requests, and automated reports that land before the weekly meeting instead of being assembled during it.

Boundaries were fixed up front: what the model may read, where it may write, and the cases where it must stop and hand over to a person.

Result

Conversion to demo request rose 28% over four months. Under NDA, so the industry and the trend are here and the brand is not.

Method: what actually automates in analytics

Models read, sort and summarise well. So what automates is not the decision — it is the preparation for it: joining data, labelling requests, drafting conclusions with checkable references.

Extraction from conversations. Loss reasons and objections sit in email and calls; pulling them out is a reliable task with a checkable result.

Labelling the inbound stream. Topic, tone, urgency — the model labels, a person audits a sample. Speed goes up without quality going down.

A draft, not a verdict. An unchecked conclusion is dangerous; the analyst gets a draft with links to the data and confirms or corrects it.

A quality set. Real examples with known answers are the only way to tell whether a change made things better or merely different.

Services used on this project: AI automation.

Relative metrics only: these projects are under NDA, so the industry, the timeframe and the direction of change are published and the brand and absolute volumes are not. On a call we walk through the same structure applied to your market: baseline, work, expected metric.