Company Context
EvenUp is a Series C legal-tech company that helps personal injury (PI) law firms generate demand letters and case narratives using a combination of AI and human review. EvenUp’s core product, Claims Intelligence, ingests medical records, bills, and case notes to produce a settlement-ready demand package and supporting analytics. The company’s customers are primarily small-to-mid PI firms (5–50 attorneys) and a growing set of large PI practices (50–300 attorneys). EvenUp operates in the US and must comply with HIPAA-adjacent expectations (handling PHI), state bar advertising rules, and strict customer confidentiality requirements.
EvenUp’s revenue model is a hybrid:
- Per-case fee (usage-based) for demand packages
- Seat-based subscription for workflow + analytics modules
- Enterprise contracts for large firms with volume commitments
EvenUp processes ~85,000 cases/month, has ~1,200 firm accounts, and is growing ~70% YoY. Competition includes Filevine/Lead Docket (case management ecosystems), Lawyaw/Documate (document automation), and newer AI drafting tools that are cheaper but less reliable.
User & Market Scenario
EvenUp’s product touches multiple personas inside a firm:
| Persona | Primary Job-to-be-Done | What they care about | Typical objections |
|---|
| Managing Partner | “Increase settlement outcomes and throughput without adding headcount.” | Profit per case, cycle time, reputation | Vendor lock-in, quality risk, cost predictability |
| Senior Attorney | “Produce a persuasive, accurate demand quickly.” | Legal quality, control, defensibility | Hallucinations, missing medical nuance |
| Case Manager/Paralegal | “Keep cases moving and reduce admin work.” | Ease of use, fewer back-and-forths | Extra steps, unclear status, rework |
| Intake/Operations | “Standardize processes and forecast capacity.” | Visibility, staffing, SLAs | Integration complexity |
Market dynamics:
- PI firms are under pressure to do more with fewer staff; paralegal hiring is tight in many metros.
- Insurers are increasingly demanding complete, well-organized medical documentation, raising the bar for demand quality.
- Firms are experimenting with AI, but trust and defensibility are key—one bad demand can damage settlement leverage.
The Problem / Opportunity
EvenUp’s leadership believes the company is at an inflection point: growth has been driven by landing new firms, but net revenue retention (NRR) is flattening.
Current business signals (last 2 quarters):
- Logo retention: 92%
- NRR: 108% (target is 125% within 12 months)
- Expansion is uneven: top 20% of firms drive 55% of revenue
- Time-to-first-value (TTFV): median 18 days from contract to first completed demand
- Repeat usage: 35% of new firms submit <5 cases in their first 60 days
User research highlights:
- Firms that integrate EvenUp into a standard operating process (SOP) submit 3–5× more cases per month.
- “Success” is perceived differently: partners focus on settlement uplift and margin, while paralegals focus on reduced rework.
- A major churn driver is not dissatisfaction—it’s inertia: firms revert to old workflows after a few cases.
Your VP of Product asks you to define what a “successful user” is for EvenUp and explicitly connect that to long-term revenue goals.
Your Task (Deliverables)
In a structured way, answer:
- Define “successful user” for EvenUp. Be explicit about:
- Which persona(s) you mean (user vs customer vs account)
- The user outcome(s) and the product behaviors that indicate success
- Segment success definitions: propose 2–3 success archetypes (e.g., small firm vs enterprise) and explain why one definition won’t fit all.
- Tie success to revenue: show the causal chain from user success → retention/expansion → long-term revenue (NRR/LTV). Include at least one back-of-envelope model.
- Prioritize product bets: propose 3 candidate initiatives that would increase the number of “successful users,” and prioritize them.
- Measurement plan: define leading indicators you’d instrument in the first 30–60 days to predict long-term success.
Constraints
- Timeline: You have 1 quarter to ship improvements; you can only staff 6 engineers, 1 designer, 1 data analyst.
- Reliability: Demand package quality must not regress; legal accuracy issues create severe reputational risk.
- Integrations: Many firms use Clio, Filevine, SmartAdvocate, or custom workflows. Deep integrations are valuable but expensive.
- Compliance & trust: You must assume customers are sensitive to AI errors and PHI handling; explainability and audit trails matter.
- Business goal: Improve NRR from 108% → 125% over 12 months without materially increasing support costs.
