How do I get Local Services Ads data into my own reporting instead of the dashboard?

Why the dashboard is not enough

The Local Services interface answers questions about Local Services. It cannot tell you which of those leads became a booked job, what the job was worth, which technician ran it, or whether the customer came back. Those facts live in your field service system, and joining them requires both sides in one place.

That is the entire reason to export. Not because the dashboard is bad, but because the interesting questions are cross-system by nature, which is what systems integration exists to solve.

Pull on a schedule and keep the raw record

A durable pipeline has a few properties that manual exports never have.

• Scheduled, not remembered. Daily is right for most shops. Anything that depends on a person will have gaps.
• Raw storage. Keep the record as returned, then transform. When a number looks wrong six months later you need the original to check against.
• Idempotent re-pulls. Credits and lead statuses change after the fact. Your pipeline must be able to re-read a past window and update rather than duplicate.
• Stable keys. Store the platform's lead identifier. It is the only reliable join key you get.

The join to operational data is where the value is

Once lead records land in your own store, you match them to bookings and jobs. The usual keys are the phone number, the timestamp, and the service address, applied in that order with a tolerance window. Expect imperfect matching and measure the match rate explicitly.

Report the unmatched share rather than distributing it silently across sources. A reporting system that admits what it does not know is far more useful in a leadership meeting than one that always adds to a hundred. This is the same discipline behind honest revenue reporting.

Plan for the platform to change

Ad platform APIs deprecate fields, rename resources and version aggressively. A pipeline built once and never touched will break, usually quietly and usually at the point where a field stops being populated rather than where a call fails.

Build monitoring into the pipeline itself: alert on zero rows, on a sudden schema change, on a match rate that falls out of its normal band. Treating API integrations as living systems rather than finished projects is the difference between reporting you trust and reporting you argue about.