nroze22 ROI Calculator Builder
Purpose
Create prospect-specific ROI analyses that quantify the financial and operational benefits of adopting Talosix EDC. The calculator should use a combination of the prospect's own data (when available) and validated industry benchmarks to build a credible, defensible business case.
When to Use
- During or after discovery to quantify pain points financially
- As part of a formal proposal to justify investment
- When a deal is stalled and needs a business case to move forward
- For executive-level conversations where financial justification is required
- At renewal to demonstrate realized value
Information Gathering
Prospect-Specific Inputs (Preferred)
Request these from the prospect when possible:
| Input | Description | If Unknown, Use Benchmark |
|---|---|---|
| Number of active studies | Concurrent studies in the portfolio | Varies by company type |
| Average sites per study | Sites across all active studies | 50-150 (Phase III) |
| Average subjects per study | Enrolled subjects per study | 200-500 (Phase III) |
| Average eCRF pages per subject | Data volume indicator | 80-150 pages |
| Average study build time | Weeks from protocol final to go-live | 10-16 weeks |
| Current query rate | Queries per 1000 eCRF fields | 8-15% |
| Average query resolution time | Days from query open to close | 5-14 days |
| SDV percentage | Source data verification rate | 80-100% |
| Cost per monitoring visit | Loaded cost per on-site visit | $1,500-$3,000 |
| Annual EDC spend | Current EDC licensing + services | Varies |
| Time from LPLV to DB lock | Weeks after last patient last visit | 4-8 weeks |
| FTE data management cost | Fully loaded annual cost per DM | $85,000-$130,000 |
| Study amendments per study | Average protocol amendments | 2-3 per study |
| Time to implement amendment | Weeks to deploy EDC amendment | 2-6 weeks |
Industry Benchmarks
Use these peer-reviewed and industry-sourced benchmarks as defaults:
Study Setup:
- Average EDC study build time: 12 weeks (Tufts CSDD)
- Cost of one week delay in study start: $37,000-$600,000 depending on therapeutic area and phase (Tufts CSDD)
- Average cost to build and validate an EDC study: $50,000-$200,000
Data Quality:
- Industry average query rate: 10-15 queries per 100 eCRF pages (SCDM benchmarks)
- Cost per query lifecycle (open, send, resolve, close): $50-$75 (industry estimates)
- Percentage of queries that are avoidable with better edit checks: 30-50%
Monitoring:
- Average monitoring visit cost (on-site): $2,000-$3,000 including travel
- Average monitoring visits per site per year: 6-10
- RBM can reduce on-site visits by 25-50% (TransCelerate benchmarks)
Database Lock:
- Average time from LPLV to DB lock: 6-8 weeks (traditional), 2-4 weeks (modern EDC)
- Cost per day of delayed NDA/BLA submission: varies by indication, can exceed $1M/day for blockbuster drugs
- Revenue at risk per month of delayed approval: drug-specific
ROI Model Components
1. Study Setup Acceleration
Current setup time: [X] weeks
Talosix projected setup time: [Y] weeks (typically 40-60% reduction)
Time saved: [X - Y] weeks per study
Studies per year: [N]
Annual time saved: (X - Y) x N weeks
Financial impact: (X - Y) x N x [cost per week of delay]
Value drivers:
- Form library with reusable components reduces design time
- No-code configuration eliminates programming bottleneck
- Integrated validation reduces separate testing cycles
- Template-based study setup for similar protocols
2. Data Query Reduction
Current query rate: [X] per 100 eCRF pages
Talosix projected query rate: [Y] per 100 eCRF pages (typically 30-50% reduction)
Total eCRF pages per study: [subjects] x [pages per subject]
Queries avoided per study: (X - Y) / 100 x [total pages]
Cost per query: $[50-75]
Annual savings: Queries avoided x N studies x cost per query
FTE impact: Queries avoided x [time per query] / [annual work hours] = FTEs redeployed
Value drivers:
- Real-time edit checks catch errors at the point of entry
- Conditional logic prevents impossible data combinations
- Smart defaults reduce transcription errors
- Better site UX reduces training-related entry errors
3. Monitoring Efficiency
Current monitoring visits per site per year: [X]
Sites across all studies: [N]
Total annual monitoring visits: X x N
Cost per visit: $[2,000-3,000]
Current annual monitoring cost: X x N x cost per visit
With Talosix RBM support:
Projected visit reduction: [25-40%]
Visits eliminated: X x N x reduction percentage
Annual savings: Visits eliminated x cost per visit
Value drivers:
- Real-time data quality dashboards enable central monitoring
- KRI tracking identifies sites needing attention vs. routine visits
- Remote data review reduces need for on-site SDV
- Automated compliance checks flag issues before monitor visits
4. Database Lock Acceleration
Current LPLV-to-lock time: [X] weeks
Talosix projected time: [Y] weeks (typically 30-50% reduction)
Time saved per study: (X - Y) weeks
For submission-critical studies:
Revenue impact per week of earlier approval: $[estimate based on drug revenue projections]
Probability-adjusted value: Revenue per week x (X - Y) x probability of approval
Value drivers:
- Continuous data cleaning during the study (not batch at end)
- Automated CDISC mapping reduces post-lock data transformation
- Real-time data quality metrics enable rolling database lock
- Fewer outstanding queries at LPLV
5. Amendment Agility
Amendments per study per year: [X]
Current time to implement amendment in EDC: [Y] weeks
Talosix projected implementation time: [Z] weeks
Time saved per amendment: (Y - Z) weeks
Annual time saved: X x N studies x (Y - Z) weeks
Impact: Reduced enrollment delays, faster protocol compliance
Financial value: Time saved x [cost per week of enrollment delay]
6. Operational Efficiency (FTE Optimization)
Current DM FTEs dedicated to EDC-related tasks: [X]
Tasks automated or reduced by Talosix:
- Query management: [X%] time reduction
- Report generation: [X%] time reduction
- Study build: [X%] time reduction
- Amendment implementation: [X%] time reduction
FTE time recaptured: Sum of reductions
Financial value: FTEs recaptured x loaded cost per FTE
ROI Summary Table
| Category | Current Annual Cost | Projected w/ Talosix | Annual Savings |
|---|---|---|---|
| Study Setup | $X | $Y | $Z |
| Data Queries | $X | $Y | $Z |
| Monitoring | $X | $Y | $Z |
| Database Lock | $X | $Y | $Z |
| Amendments | $X | $Y | $Z |
| FTE Optimization | $X | $Y | $Z |
| Total Operational Savings | $Z | ||
| Talosix Investment | ($A) | ||
| Net Annual Benefit | $B | ||
| ROI | X% | ||
| Payback Period | X months |
ROI = (Net Annual Benefit / Total Talosix Investment) x 100
Payback Period = Total Talosix Investment / (Net Annual Benefit / 12)
Sensitivity Analysis
Present three scenarios to account for uncertainty:
| Scenario | Assumptions | ROI | Payback |
|---|---|---|---|
| Conservative | Low-end improvements (25th percentile) | X% | X months |
| Expected | Mid-range improvements (50th percentile) | X% | X months |
| Optimistic | High-end improvements (75th percentile) | X% | X months |
Credibility Guidelines
- Cite sources for all industry benchmarks (Tufts CSDD, SCDM, TransCelerate)
- Use prospect's own data whenever available; benchmarks are fallbacks
- Be conservative in projections; under-promise and over-deliver
- Exclude soft benefits from the primary ROI calculation (improved site satisfaction, reduced audit risk) but mention them qualitatively
- Show your math -- every number should be traceable to an input assumption
- Acknowledge limitations -- state that actual results depend on implementation quality and adoption
Output Format
- Executive ROI Summary - One-page visual summary with headline ROI, payback period, and top 3 value drivers
- Detailed Model - Full calculation workbook with all inputs, formulas, and outputs
- Assumptions Document - Every input, its source (prospect-provided or benchmark), and confidence level
- Sensitivity Analysis - Three-scenario view showing ROI range
- Presentation Slides - 3-5 slides suitable for executive review