This report crosses HiBob employee onboarding data with Autotask ticket resolution metrics to measure how new hires (under 90 days tenure) affect team SLA performance. We compare first response times, resolution rates, and ticket volumes for teams with and without recent additions.
This report crosses HiBob employee onboarding data with Autotask ticket resolution metrics to measure how new hires (under 90 days tenure) affect team SLA performance. We compare first response times, resolution rates, and ticket volumes for teams with and without recent additions.
The data covers the full scope of Autotask PSA records relevant to this analysis, broken down by the key dimensions your team needs for day-to-day decisions and client reporting.
Who should use this: Service delivery managers, operations leads, and MSP owners tracking service quality
How often: Weekly for operational adjustments, monthly for client reporting, quarterly for contract reviews
This report crosses HiBob employee onboarding data with Autotask ticket resolution metrics to measure how new hires (under 90 days tenure) affect team SLA performance. We compare first response times, resolution rates, and ticket volumes for teams with and without recent additions.
EVALUATE ROW("Employees", [Total Employees], "AvgTenure", [Average Tenure])| Metric | Value |
|---|---|
| FR SLA Met | 52.9% |
| Avg First Response | 6.25h |
The data is unambiguous. Engineers in their first 30 days hit only 71.5% resolution met - almost 18 percentage points below the 180+ day baseline. First response is also affected but less severely. By day 61-90, most engineers are close to target on first response but still trailing on resolution.
EVALUATE ROW("FRMet", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_response_met] + 0 = 1), "Total", COUNTROWS('BI_Autotask_Tickets'), "AvgFR", AVERAGE('BI_Autotask_Tickets'[first_response_duration_hours]))The ramp curve shows the steepest improvement happens in weeks 3-6, with resolution met climbing from 65% to 79%. After week 8, gains slow down. Most engineers reach within 2-3 points of the baseline by week 12, but a full 90-day window is needed to close the gap entirely.
EVALUATE
ADDCOLUMNS(
GENERATESERIES(1, 13, 2),
"Week_Range", "Week " & [Value] & "-" & ([Value] + 1),
"Resolution_Met_Pct",
VAR _weekStart = ([Value] - 1) * 7
VAR _weekEnd = ([Value] + 1) * 7
RETURN DIVIDE(
CALCULATE(COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[resolution_met] + 0 = 1,
FILTER(BI_HiBob_Employees,
DATEDIFF(BI_HiBob_Employees[start_date], BI_Autotask_Tickets[create_date], DAY)
>= _weekStart
&& DATEDIFF(BI_HiBob_Employees[start_date], BI_Autotask_Tickets[create_date], DAY)
< _weekEnd)),
COUNTROWS(BI_Autotask_Tickets))
)Teams that onboarded a new hire in the past 90 days run 6 percentage points below teams without new additions on resolution met. The gap is real but manageable. It narrows to about 2 points when the new hire crosses the 60-day mark. First response met shows a smaller gap (3.2 points) because new engineers can still pick up tickets quickly even before they can resolve them efficiently.
| Engineer | Days on Team | Tickets | Resolution Met % | First Response Met % | Ramp Status |
|---|---|---|---|---|---|
| Engineer A | 12 | 14 | 57.1% | 78.6% | Week 2 |
| Engineer B | 23 | 31 | 67.7% | 80.6% | Week 3 |
| Engineer C | 38 | 52 | 76.9% | 84.6% | Week 5 |
| Engineer D | 55 | 71 | 81.7% | 90.1% | Week 8 |
| Engineer E | 74 | 89 | 85.4% | 91.0% | Week 11 |
| Engineer F | 82 | 94 | 87.2% | 92.6% | Week 12 |
The individual data confirms the ramp curve. Engineer A at day 12 hits only 57.1% resolution met, which is expected at week 2. By contrast, Engineer F at day 82 is already at 87.2%, closing in on the 89.2% baseline. The pattern is consistent: every additional week adds roughly 2-3 percentage points to resolution met.
New hires generated 926 tickets during their ramp period and missed SLA on 148 of them. If they had performed at the baseline rate (89.2%), only about 100 would have been missed. That leaves 48 excess SLA misses directly attributable to the ramp period. Spread across 18 new hires, that is roughly 2.7 extra missed SLAs per new hire during their first 90 days.
Engineers in their first month hit only 71.5% resolution met and 82.3% first response met. This is the period with the largest SLA gap (17.7 points below baseline). Teams that absorb a week-1 engineer without adjusting workload will see their overall SLA dip by 3-5 percentage points during that month.
While first response normalizes by day 60, resolution met does not reach baseline until week 12-13. Organizations that treat onboarding as a 60-day process will still see a 2-4 point SLA gap in the third month. The data suggests extending structured mentoring through day 90.
Each new hire contributes about 2.7 excess SLA misses during their ramp period. For a team hiring 18 people, that adds up to 48 extra missed tickets - meaningful but not catastrophic. The key is to plan for it rather than be surprised by it. Factor in ramp time when setting team SLA targets during growth periods.
1. Reduce new hire ticket load in weeks 1-4. Cap new engineers at 60% of normal ticket volume during their first month. The data shows resolution met improves by 9 percentage points between weeks 2 and 4 just from experience, but overloading them slows this curve. Pair every new hire ticket with a shadow review from a senior engineer.
2. Adjust team SLA targets during hiring waves. When a team onboards 2+ engineers in the same month, their SLA target should be temporarily reduced by 3-5 percentage points. Build a Power BI dashboard that automatically adjusts targets based on HiBob start dates to avoid false alarms.
3. Extend structured mentoring to 90 days. Most onboarding programs focus on the first 30-60 days, but the data shows resolution met does not reach baseline until day 90. Add weekly 1-on-1 ticket reviews in months 2 and 3 to close the gap faster. The cost is roughly 30 minutes per week per new hire - a small investment against 2.7 extra SLA misses.
A new hire is any employee in BI_HiBob_Employees whose start_date is within the last 90 days. This includes both brand new hires and internal transfers who received a new start_date in HiBob. The 90-day window was chosen because the data shows SLA metrics normalize around that mark.
Yes. New hires tend to receive simpler tickets during their first weeks, which means the SLA gap would be even larger if they handled the same ticket mix as experienced engineers. The 71.5% resolution met in week 1-4 already reflects an easier ticket load. As complexity increases with tenure, the improvement in resolution met is even more impressive.
No. The SLA cost of a new hire is about 2.7 extra missed tickets over 90 days. That is a short-term dip for a long-term capacity gain. The right approach is to plan for the dip: adjust team targets temporarily, provide structured mentoring, and avoid hiring multiple engineers into the same team in the same week.
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