A Spaid ServiceTitan consultant connects to your ServiceTitan data via API, pulls 6–12 months of job records, invoice history, dispatch patterns, and call data, and surfaces the revenue gaps your standard reports miss — in 30 days, with a dollar amount attached to each one.
ServiceTitan captures more operational data than any other FSM on the market. Most operators use 15–20% of the analytical capability they’re paying for. 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 ServiceTitan collects and the decisions your ops team needs to make. That layer is ServiceTitan optimization — reading your FSM data, cross-referencing it with call and invoice records, and turning the patterns into daily operational decisions. It requires an analyst who knows where to look and a system that monitors it continuously.
ServiceTitan captures every job, invoice, dispatch decision, pricebook lookup, and membership interaction. Most operators use 15–20% of the analytical capability they’re paying for. The data to find margin drift, callback root causes, and CSR booking gaps exists in the system. It just hasn’t been connected and analyzed.
Less than 20% of ServiceTitan’s data is actively analyzed by the average operatorServiceTitan’s native reports show job counts, revenue totals, and technician activity. They don’t cross-reference callback rate by tech and job type, GM variance by dispatcher and season, or CSR booking rate against inbound call volume from your call tracking system. Those patterns exist in the data. They require an analyst who knows where to look.
The 5 ServiceTitan reports operators actually need aren’t in the standard dashboardPricebook compliance drops after a few months. Techs stop filling in required fields. Dispatch patterns that worked in Year 1 don’t match Year 3 job mix. Without a monitoring layer, ServiceTitan slowly becomes a job-tracking system instead of a margin-management system.
$200K–$400K in annual margin drift from unmonitored ServiceTitan executionServiceTitan training teaches your team how to use the system. ServiceTitan intelligence tells you what the system is showing you — and what it means for your EBITDA.
Teaches button clicks, not business intelligence. Nobody leaves knowing how to use ST data to find margin drift by job type.
Manages data entry and basic reporting. Rarely has the analytical depth to surface patterns across 12 months of job, call, and invoice data simultaneously.
Answers product questions. Doesn’t tell you that your third-party HVAC installs have a 3× higher callback rate than your first-party jobs, or what that’s costing you.
Don’t know ServiceTitan’s data model. Can’t pull job-level margin by tech without a custom export and a spreadsheet process that breaks monthly.
6–12 months of job records, invoices, dispatch history, pricebook, and membership data. All of it, connected, before Week 1 ends.
Connects CallRail, ServiceTitan Phones Pro, or RingCentral to job records. See booking rate by CSR, call-to-job conversion, and follow-up gaps that are invisible inside ServiceTitan alone.
Callback rate by tech and job type, GM variance by dispatcher, quote consistency by branch. The exact patterns your standard ST reports don’t show.
Daily monitoring of pricebook compliance, field execution consistency, and booking 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 ServiceTitan via API and pulls 6–12 months of job records, invoices, pricebook history, dispatch assignments, membership data, and call logs. 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.
Connects your ServiceTitan call data (Phones Pro, CallRail, or RingCentral) to job records. AI maps call-to-booking conversion by CSR, missed call patterns by day and hour, and follow-up gaps between CSR and dispatch. The cross-system view of your ServiceTitan shop — surfaced in the first 30-day audit.
During the first 30 days, the consultant documents how your highest-margin techs use ServiceTitan — how they price, what they document, what they attach, how they close. Converts that into a repeatable standard for every tech in your shop. Embedded in the FSM job card — not a training deck.
Monitors pricebook compliance, GM per job by tech, callback rate by job type, and CSR booking rate — daily. Flags when a tech stops following the pricing standard, when a job type is trending high on callbacks, or when a CSR’s booking rate drops. Weeks before it shows up in a monthly ServiceTitan report.
Most operators on ServiceTitan have worked with at least one consultant, implementation partner, or training program that didn’t move the needle. The difference usually comes down to four criteria. Use them to evaluate anyone you’re considering.
An export is a snapshot. A consultant working from a CSV export you prepared sees a frozen slice of your data — filtered through whatever you thought to include. A consultant connected to your ServiceTitan API sees every job record, invoice line item, pricebook lookup, dispatch assignment, and call log for the past 6–12 months. The difference in what they can find is significant. Pattern-level analysis — GM per job by tech cross-referenced with job type and season — requires the full data set, not a filtered export.
ServiceTitan data tells you what happened. On-site observation tells you why. A consultant who reviews dashboards remotely will find the patterns the data shows. They won’t find the dispatching habit that’s routing your lowest-margin tech to your highest-value neighborhoods, or the CSR behavior that’s inflating missed-call rate on Tuesday mornings. On-site embed — ride-alongs, call center observation, job close review — is what separates a data report from a root-cause map.
Execution drifts. Pricebook compliance rates drop after a few months. Techs who adopted the new pricing standard in Month 1 stop following it by Month 4 without monitoring. A consultant who delivers a report and exits has identified the problem at a point in time. The operation is already drifting back toward the baseline. Ask specifically: what monitoring mechanism stays connected after the audit? Daily drift detection on pricebook compliance, GM per job by tech, and CSR booking rate is the difference between a one-time finding and a sustained margin improvement.
ServiceTitan can be configured correctly and still produce poor margins. The pricebook can be built, the workflow rules set, the membership templates in place — and your third-party HVAC installs can still run a 28% callback rate because of a field execution pattern that no configuration change will fix. A consultant who defaults to configuration recommendations when the real problem is execution is solving the wrong problem. The question to ask: can they show you, with your own data, the specific tech-job type-season combinations where margin is below target — and can they explain why?
From API connection to deployed margin guardrails in four weeks. Every step has a deliverable. The $200K guarantee is measured at Week 4.
The forward-deployed engineer arrives on-site at your operation. Week 1 includes dispatch board ride-alongs, call center observation sessions, job close reviews with field techs, and full ServiceTitan API connection. The API pull covers 6–12 months of job records, invoice history, pricebook data, dispatch assignments, membership data, and call logs — no manual exports, no admin burden. By end of Week 1, Spaid has full data access and the on-site context to interpret what the data shows.
The API data is analyzed across three primary dimensions: GM per job by tech cross-referenced with job type (which techs are underperforming on which job categories, and by how much), callback root cause by trade and job type (which job types are generating disproportionate callbacks and what the field pattern driving them is), and CSR booking gap by rep (which reps have the lowest call-to-booking conversion rate and what time-of-day or job-type patterns are driving it). The cross-system call data integration surfaces booking gaps that are invisible inside ServiceTitan’s standard reports.
Every identified gap gets a dollar value. The GM variance by tech gets annualized and converted to recoverable margin if brought to your top-quartile standard. The callback cost per job type is calculated using loaded tech cost and return-visit time. The CSR booking gap is converted to lost revenue using your average ticket by job category. The output is a ranked priority list: the five to eight highest-dollar gaps in your operation, each with a specific dollar estimate, the data behind it, and the operational change required to close it.
The knowledge graph draft documents how your highest-margin technicians use ServiceTitan — how they price, what they document, how they close — and converts it into a repeatable standard embedded in the FSM job card. Margin guardrails are configured directly in ServiceTitan’s workflow: pricebook compliance alerts, job-type pricing thresholds, and follow-up automation triggered from ST job status. The audit deliverable package is handed to your ops leader. Daily monitoring begins. The $200K guarantee is evaluated at this stage.
ServiceTitan stores your pricebook. Your technicians may not be using it. Pricebook drift takes three forms: technicians using flat-rate prices that haven’t been updated to reflect current material costs or labor rates, technicians manually overriding pricebook prices at the point of sale, and technicians bypassing the pricebook entirely and entering custom line-item prices. Any one of these patterns compounds silently. All three running simultaneously in the same operation can represent $40K–$80K in annual margin leakage before it appears in a monthly P&L review.
The Spaid API connector pulls every invoice line item from your ServiceTitan data and surfaces pricebook compliance rate by individual technician and by job type. The analysis shows not just who is non-compliant, but the specific job categories where deviation is highest and the exact dollar difference between your pricebook standard and what was actually invoiced. Pricebook compliance is one of the fastest gaps to close once it’s quantified — because the fix is operational, not a software change.
Most operators discover that pricebook drift has been compounding for 12–18 months before the audit surfaces it. After the first 30 days, Spaid’s daily monitoring flags compliance rate drops at the tech level before they compound into a quarterly margin problem.
A ServiceTitan consultant connects to your FSM via API, pulls 6–12 months of job, invoice, dispatch, and membership data, and surfaces the margin patterns your standard reports miss. Spaid’s forward-deployed engineer embeds on-site — riding along with techs, observing call center operations, reviewing job close data — and builds a pattern-level intelligence layer on top of your existing ServiceTitan data. The cross-system analysis surfaces GM per job by tech and job type, callback root cause, pricebook compliance rate by tech, and CSR booking gap by rep. The output is a ranked dollar-value list of every margin leak in your operation, with standards deployed back into ServiceTitan’s workflow so the findings don’t stay in a report.
ServiceTitan is the best FSM on the market. It stores and organizes your operational data better than any alternative. But storing data and reading it at the pattern level are two different problems. ServiceTitan’s built-in reports show activity — job counts, revenue totals, technician hours. They don’t surface GM variance by tech cross-referenced with job type and season, callback root cause by trade, or pricebook compliance rate by individual technician. Those patterns exist in your ServiceTitan data. They require an analyst who knows where to look and a monitoring layer that catches drift before it compounds. Most $20M–$100M operators on ServiceTitan are leaving $200K–$400K per year in recoverable margin on the table because the analysis layer is missing.
A ServiceTitan implementation partner configures the software — pricebook setup, workflow rules, dispatch board layout, membership templates. That is a configuration problem. Spaid solves an execution and intelligence problem. We connect to your already-configured ServiceTitan via API, read your actual operational data at the pattern level, quantify every margin gap with a dollar value, and deploy the standards back into your ST workflow. Implementation partners hand off a configured system. Spaid embeds a forward-deployed engineer who monitors execution drift daily and surfaces the patterns your built-in reports don’t show. ServiceTitan can be configured correctly and still produce poor margins — Spaid finds the execution gaps that configuration changes won’t fix.
Pricebook compliance analysis starts with the ServiceTitan API connector pulling every invoice line item for the past 6–12 months. The analysis surfaces three things: which technicians are using flat-rate pricebook prices versus manually overriding them, which job types have the highest deviation between pricebook price and actual invoiced amount, and how often techs are bypassing the pricebook entirely and entering custom prices. Spaid cross-references compliance rate against GM per job — so you see not just who is non-compliant, but exactly what it’s costing in margin. Pricebook drift is one of the top three margin leaks in ST-powered operations and typically compounds undetected for 6–18 months before showing up in a P&L. After the audit, daily monitoring flags compliance drops at the tech level before they compound.
The first 30 days are the Full-Operation Audit: API connection in 72 hours, on-site embed in Week 1, pattern analysis and dollar quantification in Weeks 2–3, knowledge graph draft and margin guardrails deployed in Week 4. After the audit, Spaid operates as an ongoing intelligence layer — daily monitoring of pricebook compliance, GM per job by tech, callback rate by job type, and CSR booking rate. Most operators see the first recoverable margin identified within 10–14 days of data access. The $200K guarantee is measured at the 30-day mark. We ask for about 30 minutes per week of your ops leader’s time during the audit phase.
Spaid guarantees identification of at least $200,000 in recoverable annual revenue within the first 30 days — or we refund Phase 1 in full. Across $20M–$100M operators on ServiceTitan, the most common sources of recoverable margin are: unbilled and underpriced work ($80K–$150K average), pricebook non-compliance by tech ($40K–$80K average), CSR booking rate gaps surfaced by cross-referencing call data with ST job records (10–18 point lift in booking rate), and callback cost reduction from root-cause identification by job type. Total recoverable margin typically ranges from $200K to over $600K in Year 1, depending on operator size and how long execution drift has been compounding. You keep all audit deliverables regardless of outcome.
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, 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 ServiceTitan account, show you the biggest leaks, and scope the engagement. Full API access happens only if you proceed to the audit.