When PI firms evaluate a revenue intelligence platform, one of the first questions that comes up is: “What does it actually need from our case management system?” It's a smart question. Your CMS is the operational core of your firm, and understanding what data a revenue intelligence platform needs — and what it does with that data — tells you a lot about whether your current system can support it.
Here's the complete breakdown: what data matters, why it matters, and what happens when it's missing or inconsistent.
The Core Data Requirements
1. Lead Source — The Foundation of Everything
Revenue intelligence lives or dies on lead source data. Every lead that enters your firm needs to be tagged with where it came from — specifically, which vendor, which channel, or which campaign generated it. This is the field that connects marketing spend to case outcomes.
Without clean lead source data, cost per case is impossible to calculate accurately. You can calculate an average cost per case for the firm — but you can't calculate it by vendor, which is the only version that drives useful budget decisions.
What good looks like in your CMS: a structured dropdown field (not freetext), required on every lead record, with values that map to your actual vendor list.
2. Lead Date — When the Lead Arrived
The date a lead entered your firm establishes the starting point for the timeline. Revenue intelligence tracks the journey from initial lead to signed case to eventual settlement — a journey that spans 6 to 18 months in PI. The lead date is the anchor for that timeline.
This field is usually automatic in most CMS platforms. The risk is when leads are entered retroactively — days after they arrived — which distorts both volume reporting and conversion time calculations.
3. Lead Disposition — What Happened to the Lead
Every lead gets a disposition: signed, rejected, withdrawn, pending, or some equivalent. This is arguably the most important operational data point for revenue intelligence, because it determines conversion rate — and conversion rate at the source level is the primary signal for vendor quality.
What good looks like: a structured status field updated in real time as intake decisions are made, with specific disposition codes (not just “closed” or “no go”). Rejection reasons are especially valuable — knowing that 40% of rejections from a specific vendor are “outside geography” versus “not liable” tells you something different about that vendor's lead quality.
4. Signed Date — When the Case Was Signed
The date a lead converted to a signed case tells you the conversion timeline — how long your intake process takes and how that varies by lead source. It also creates the denominator for cost-per-case calculations: if 30 leads signed in a given month across three vendors, you can calculate the cost per signed case for each.
This field is standard in CMS platforms like LeadDocket and Filevine. In more general-purpose CRMs like Salesforce or HubSpot, it requires a custom field mapped to the pipeline stage change.
5. Case Type / Practice Area
Personal injury encompasses auto accidents, premises liability, product liability, workers' comp, and more. Case type data lets revenue intelligence surface whether a particular vendor sends strong auto cases but weak premises cases — or whether your cost per case for auto is $900 but for premises liability it's $2,400.
Most CMS platforms capture this. The key is consistency: if your intake team categorizes the same case type differently in different records, aggregated analysis becomes unreliable.
6. Case Severity Indicators
Not all signed cases are equal. A vendor who sends 10 cases per month but all are soft-tissue minor impact claims looks different from a vendor who sends 6 cases but half involve surgery or liability disputes. Severity indicators — injury type, medical treatment involvement, whether the case has clear liability — help revenue intelligence move from “cost per signed case” toward “cost per quality case.”
This data is often captured inconsistently, which is why it's treated as a more advanced data requirement. Many firms start with the basics and add severity data as their intelligence matures.
7. Settlement Amount and Date — The Long-Game Metric
The most valuable revenue intelligence calculation is average settlement value per lead source — which vendor's cases actually generate the most revenue per signed case, not just the most signed cases. That calculation requires settlement amount and settlement date in your CMS.
Given the 6 to 18 month lag between signing and settlement, this data takes time to accumulate. But once it does, it changes vendor evaluation entirely. A vendor with a higher cost per case but higher average settlements may be your best performer. You can't know that without settlement data.
What good looks like: a settlement amount field that gets populated when the case closes, stored in a queryable format (not just embedded in documents), and linked back to the original lead source.
Data That's Helpful But Not Required on Day One
The following data points add value but aren't required for basic cost-per-case reporting:
- Attorney assigned — Useful for understanding whether certain vendors produce cases that go to your strongest closers or create workload imbalances
- Time-to-sign — How long from lead arrival to signed case by source; longer cycles by vendor may indicate lead quality issues
- Rejection reason codes — Helps diagnose vendor quality issues at a more granular level than rejection rate alone
- Geographic data — Especially useful for multi-market firms comparing vendor performance across locations
| Missing Field | Impact | Fix Difficulty | |
|---|---|---|---|
| Lead source (20% blank) | CPC underestimated, vendor decisions skewed | Medium — requires intake policy change | |
| Rejection reason codes | Cannot diagnose vendor quality problems | Easy — add structured field to CMS | |
| Settlement amounts | Cannot calculate value-adjusted ROI | Hard — requires structured data entry process | |
| Disposition updates | Phantom conversion rates, stale data | Medium — enforce real-time status updates |
What Happens When Key Data Is Missing
The most common gaps in CMS data — and their downstream impact:
- Missing lead source on 20% of records: Cost per case by vendor is underestimated for those sources and the unattributed pool inflates your average. Your vendor decisions are based on incomplete data.
- No rejection reason codes:You know rejection rate by vendor but not why. You can't tell whether Vendor B's high rejection rate is a geographic mismatch (fixable) or a lead quality problem (systemic).
- Settlement amounts in documents, not fields:Average settlement per source is uncalculable without structured data. That's the most powerful revenue intelligence metric, and it remains inaccessible.
- Inconsistent disposition status updates:Cases that sit in “pending” for months because intake coordinators didn't update the status create phantom conversion numbers. Your signed case count looks lower than it is; your rejection rate looks higher.
The Data Audit Is Step One
Before implementing a revenue intelligence platform, the most valuable thing you can do is audit these fields in your CMS for completeness and consistency. You don't need perfect data — but you do need to know where your gaps are so you can close them during implementation rather than discovering them after.
Firms that run a data audit before implementation get to useful cost-per-case reporting significantly faster than those that skip it. The audit is not glamorous work, but it's the work that determines whether your revenue intelligence investment pays off in 90 days or six months.
If you're running on LeadDocket, a significant portion of this structure is already in place — which is why LeadDocket + RevenueScale is often the fastest path to accurate cost-per-case reporting.
Want to understand what your CMS data looks like against these requirements? Book a demoand we'll walk through a pre-integration data readiness review with your specific system.
Related guide: See our complete guide to revenue intelligence for PI firms — the four layers, the maturity model, and what RI replaces in your current stack.