Jobber operators at $5M–$25M are sitting on 6–12 months of job, invoice, dispatch, and client data that the standard Jobber reports don’t surface at the level that matters. A Spaid Jobber consultant connects via API, cross-references your data across field and back-of-house, and finds the $200K–$600K in recoverable revenue your Jobber dashboard shows but doesn’t explain.
Jobber captures every job, invoice, dispatch decision, and client interaction. Most operators at $5M–$25M use the revenue summary and job counts. The patterns that explain margin drift, callback root causes, and CSR booking gaps are already in the system. They just haven’t been connected and analyzed.
The gap isn’t the software. It’s the layer between the data Jobber collects and the decisions your ops team needs to make. That layer is Jobber optimization — reading your FSM data, cross-referencing it with call and invoice records, and turning the patterns into daily operational decisions.
You can see revenue by client and jobs by team member. You can’t see GM variance by tech × job type, callback root cause, or which service category is generating the most rework. Getting to the pattern requires joining invoice and job cost data at the line-item level — which is what the API connection does on Day 1.
8–12 point GM spread between top and bottom performers on identical job typesAt $5M–$10M, weekly P&L review is sufficient. At $15M–$25M, you’re managing 20+ techs, multiple branches, and performance variance that needs to be tracked at the individual level. Jobber’s built-in reporting wasn’t designed for this — the data is there, but the analysis layer isn’t.
60% of operators at $15M+ on Jobber have never run GM by tech analysisMost Jobber operators who move to ServiceTitan or HCP prematurely don’t solve their margin problems. The problem isn’t the tool — it’s that nobody has read the data they already have. Getting the operational intelligence from your current Jobber instance is faster and cheaper than a platform migration.
$50K–$150K average cost of premature FSM migration (implementation + disruption)Jobber standard reports show what happened. Jobber data analysis — via API, cross-referenced with call recordings and invoice line items — shows why it happened and what it’s costing you.
Show revenue by client and job counts by team member. Don’t surface GM by tech × job type, callback root cause, or which service category is generating rework.
Time-consuming, break monthly, and never get to the level of resolution needed. Nobody is running GM variance by tech and job type from a Jobber CSV export on a regular cadence.
Teaches your team how to use the system. Doesn’t tell you that your HVAC maintenance callbacks are running 3× higher than your installs, or what that’s costing you in annual margin.
Don’t know Jobber’s data model. Can’t join job cost and invoice data at the line-item level without a custom export process that won’t survive the first ops change.
6–12 months of job records, invoices, dispatch history, and client data. All of it, connected, before Week 1 ends.
Connects CallRail, RingCentral, or similar to job records. See booking rate by CSR, call-to-job conversion, and follow-up gaps that are invisible inside Jobber alone.
Callback rate by tech and job type, GM variance by team member, quote conversion by service category. The exact patterns your standard Jobber reports don’t show.
Daily monitoring of GM per job, booking rate, and callback rate trends. Catches drift before it compounds into a monthly P&L problem.
Full API access in 72 hours. Cross-system analysis before Week 1 ends. Revenue gap map in 30 days.
Connects to Jobber via API and pulls 6–12 months of job records, invoices, client history, dispatch assignments, and team member data. No manual exports, no admin time, no IT project. The Spaid consultant has full data access before Week 1 ends — and the analysis starts immediately. GM per job by tech and job type, callback patterns, quote conversion, and team performance metrics surfaced automatically.
Connects to your telephony (CallRail, RingCentral, or similar) and cross-references call outcomes with Jobber job data. AI maps booking rate by CSR, identifies where inbound calls are dropping, and surfaces the gap between call volume and jobs booked. The cross-system view of your operation — surfaced in the first 30-day audit.
Built from Jobber data and ride-alongs, the operational knowledge graph codifies how your highest-margin techs execute by job type — how they price, what they document, how they close. Deploys as a briefing layer for every tech before they arrive on-site. Not a training deck. Embedded in the job workflow.
Monitors GM per job, booking rate, and callback rate from your Jobber data daily. Flags variance before it compounds into a monthly P&L problem — weeks before it shows up in a Jobber summary report. Continuous, not quarterly.
Our Full-Operation Audit (Days 1–30) maps every revenue leak — field and back of house. If we don’t identify at least $200,000 in recoverable annual revenue from your Jobber data, we refund Phase 1 in full. You keep all audit deliverables.
After kickoff, we ask for about 30 minutes a week of your ops leader’s time.
We’ll start with a recent export or sample data from your Jobber account, show you the biggest leaks, and scope the engagement. Full API access happens only if you proceed to the audit.
Already on ServiceTitan? The same analysis layer applies — API connection, GM variance by tech × job type, callback root cause, and CSR booking gaps. With more data in the system, there’s more to find.
HouseCall Pro operators at $5M–$20M face the same reporting gap. The data is in the system. The analysis layer is missing.
FieldEdge operators running HVAC and plumbing at $10M–$40M have job and invoice data that their standard reports don’t surface at the margin level that matters.