What is Client Intelligence?
Client Intelligence provides data-driven insights into how each client’s work typically progresses:- Development Score: How development tasks perform against estimates
- Design Score: How design tasks perform against estimates
- Help Desk Score: How help desk tickets perform against estimates
- QA Score: How many QA cycles a client typically requires
Understanding Scores
Score Values
A score of 1.00 means the client’s work matches estimates perfectly:| Score | Meaning | Example |
|---|---|---|
| 1.00 | On target | Work matches quoted time exactly |
| 1.15 | 15% slower | A 4-hour quote typically takes ~4.6 hours |
| 0.90 | 10% faster | A 4-hour quote typically takes ~3.6 hours |
Confidence Levels
Scores become more reliable with more data:| Confidence | Sample Size | Reliability |
|---|---|---|
| Low | Fewer than 5 tasks | Use with caution - fall back to maximum estimates |
| Medium | 5-15 tasks | Reasonable confidence - can use for scheduling |
| High | 15+ tasks | Reliable data - use for accurate scheduling |
The system uses a weighted average with more recent tasks having greater influence. This means scores naturally adapt as client patterns change over time.
How Scores Affect Scheduling
Two-Layer Planning Model
CharleOS uses a two-layer planning model:PM Budget Planning
Uses: Quoted AveragesWhen PMs plan which tasks fit in a month’s budget, they use average estimates from t-shirt sizes. This determines what the client “pays for”.
Team Scheduling
Uses: Score-Adjusted EstimatesWhen scheduling actual work, the system uses score-adjusted estimates or maximums. This ensures realistic capacity planning.
Example
A Medium task (3-6 hours, average 4.5):- Budget: PM allocates 4.5 hours from client’s monthly hours
- Scheduling: For a client with score 1.2, system suggests 5.4 hours for developer scheduling
Viewing Client Intelligence
Client Intelligence is displayed on the Intelligence tab of each client’s detail page.What You’ll See
- Development Score Card: Score value with confidence badge and sample size
- Design Score Card: Score value with confidence badge and sample size
- QA Score Card: Average QA cycles with first-pass rate and sample size
- Score History: Chart showing score trends over time with a filterable time range (3M/6M/12M/All), plus an event log of score changes
- Outlier Tasks: Table of tasks excluded from calculations, with the ability for admins/managers to include or exclude them
Interpreting the Data
- Client Faster Than Average
- Client On Target
- Client Slower Than Average
Score below 1.0 (e.g., 0.85)What it means:
- This client’s work typically completes faster than estimated
- Could indicate efficient processes or clear requirements
- Good candidate for tighter scheduling
- Can schedule closer to average estimates
- May indicate estimation is too conservative for this client
Outlier Tasks
Tasks that deviate significantly (more than 130%) from quoted estimates are flagged as outliers. These are excluded from score calculations to prevent anomalies from skewing the data.Why Tasks Become Outliers
Common reasons for outlier status:- Unexpected scope expansion
- External dependencies or blockers
- Initial estimate was significantly wrong
- Client-side delays
Managing Outliers
Outliers are displayed in the Client Intelligence section. Admins can:- Review why the task was flagged
- Include/exclude outliers from calculations if needed
- Use the data to improve future estimating
Help Desk Scoring
Help desk tickets have their own scoring system, separate from task scores:- Based on estimated time vs actual time logged
- Helps AI suggest more accurate time estimates for new tickets
- Improves PM scheduling efficiency
When a help desk ticket is created, Alan automatically suggests a time estimate based on the ticket content and client history. This suggestion appears in the ticket details for the PM.
QA Scoring
QA Scoring tracks how many QA cycles a client’s work typically requires:- First-Pass Rate: Percentage of tasks that pass QA on first review
- Average Cycles: Mean number of review cycles needed
- QA Score: Multiplier for adjusting QA time estimates
How It Affects Scheduling
- T-Shirt Suggestions: High QA scores may trigger recommendations to size up
- Subtask Estimates: QA subtasks get adjusted time based on QA score
- AI Context: QA metrics are included in AI suggestion reasoning
A QA score of 1.8 means the client typically needs ~80% more QA time than average. The system adjusts subtask suggestions accordingly.
Related Documentation
QA Scoring
Detailed guide to QA scoring and its effects
Billing Model
How scores fit into the overall billing system
Scheduling
How score-adjusted estimates appear in scheduling
T-Shirt Sizing
Understanding task size estimates
Help Desk
Help desk ticket workflow and AI suggestions