Bicycle.LLC Schedule a call

← Dashboard collection

Choose dashboard — Engagement and Exit

people analytics dashboard

Engagement and Exit

Survey, participation, segment, climate, driver, and exit-survey measures.

9 pages · Power BI

These dashboards use synthetic workforce data. Values illustrate the report design.

Choose page — 8 Climate and belonging

Selected page

8 Climate and belonging

DAX definitions

Exact source expressions and direct measure dependencies for the selected page.

Burnout High-Exhaustion %

ClimateResponse · Climate and belonging

VAR __rows = FILTER(ClimateResponse, ClimateResponse[wave] = 2023 && ClimateResponse[factor] = "Burnout / Exhaustion" && NOT ISBLANK(ClimateResponse[score]) && ClimateResponse[score] >= 1 && ClimateResponse[score] <= 5)
VAR __people = GROUPBY(__rows, ClimateResponse[respondentId], "__items", COUNTX(CURRENTGROUP(), ClimateResponse[score]), "__mean", AVERAGEX(CURRENTGROUP(), ClimateResponse[score]))
VAR __complete = FILTER(__people, [__items] = 4)
VAR __n = COUNTROWS(__complete)
RETURN IF(__n >= 10, DIVIDE(COALESCE(COUNTROWS(FILTER(__complete, [__mean] >= 4)), 0), __n), BLANK())

Direct dependencies: None

Climate Respondents

ClimateResponse · Climate and belonging

VAR __rows = FILTER(ClimateResponse, ClimateResponse[wave] = 2023 && NOT ISBLANK(ClimateResponse[score]) && IF(ClimateResponse[factor] = "eNPS", ClimateResponse[score] >= 0 && ClimateResponse[score] <= 10, ClimateResponse[score] >= 1 && ClimateResponse[score] <= 5)) VAR __n = COUNTROWS(SUMMARIZE(__rows, ClimateResponse[respondentId])) RETURN IF(__n >= 10, __n, BLANK())

Direct dependencies: None

Fairness Favorable %

ClimateResponse · Climate and belonging

VAR __rows = FILTER(ClimateResponse, ClimateResponse[wave] = 2023 && ClimateResponse[factor] = "Fairness / Organizational Justice" && NOT ISBLANK(ClimateResponse[score]) && ClimateResponse[score] >= 1 && ClimateResponse[score] <= 5)
VAR __n = COUNTROWS(SUMMARIZE(__rows, ClimateResponse[respondentId]))
RETURN IF(__n >= 10, DIVIDE(COALESCE(COUNTROWS(FILTER(__rows, ClimateResponse[score] >= 4)), 0), COUNTROWS(__rows)), BLANK())

Direct dependencies: None

Fairness Favorable % (Prior Wave)

ClimateResponse · Climate and belonging

VAR __rows = FILTER(ClimateResponse, ClimateResponse[wave] = 2022 && ClimateResponse[factor] = "Fairness / Organizational Justice" && NOT ISBLANK(ClimateResponse[score]) && ClimateResponse[score] >= 1 && ClimateResponse[score] <= 5)
VAR __n = COUNTROWS(SUMMARIZE(__rows, ClimateResponse[respondentId]))
RETURN IF(__n >= 10, DIVIDE(COALESCE(COUNTROWS(FILTER(__rows, ClimateResponse[score] >= 4)), 0), COUNTROWS(__rows)), BLANK())

Direct dependencies: None

Fairness Favorable % Change vs Prior Wave (points)

ClimateResponse · Climate and belonging

VAR __a = [Fairness Favorable %] VAR __b = [Fairness Favorable % (Prior Wave)] RETURN IF(ISBLANK(__a) || ISBLANK(__b), BLANK(), 100 * (__a - __b))

Direct dependencies: Fairness Favorable %, Fairness Favorable % (Prior Wave)

Fairness Gap Non-majority minus Majority (points)

ClimateResponse · Climate and belonging

VAR __a = CALCULATE([Fairness Favorable %], KEEPFILTERS(ClimateResponse[ethnicityRollup] = "Non-majority (all other)")) VAR __b = CALCULATE([Fairness Favorable %], KEEPFILTERS(ClimateResponse[ethnicityRollup] = "Majority (White)")) RETURN IF(ISBLANK(__a) || ISBLANK(__b), BLANK(), 100 * (__a - __b))

Direct dependencies: Fairness Favorable %

Fairness Gap Women minus Men (points)

ClimateResponse · Climate and belonging

VAR __a = CALCULATE([Fairness Favorable %], KEEPFILTERS(ClimateResponse[gender] = "Female")) VAR __b = CALCULATE([Fairness Favorable %], KEEPFILTERS(ClimateResponse[gender] = "Male")) RETURN IF(ISBLANK(__a) || ISBLANK(__b), BLANK(), 100 * (__a - __b))

Direct dependencies: Fairness Favorable %

Inclusion Favorable %

ClimateResponse · Climate and belonging

VAR __rows = FILTER(ClimateResponse, ClimateResponse[wave] = 2023 && ClimateResponse[factor] = "Inclusion & Belonging" && NOT ISBLANK(ClimateResponse[score]) && ClimateResponse[score] >= 1 && ClimateResponse[score] <= 5)
VAR __n = COUNTROWS(SUMMARIZE(__rows, ClimateResponse[respondentId]))
RETURN IF(__n >= 10, DIVIDE(COALESCE(COUNTROWS(FILTER(__rows, ClimateResponse[score] >= 4)), 0), COUNTROWS(__rows)), BLANK())

Direct dependencies: None

Inclusion Favorable % (Prior Wave)

ClimateResponse · Climate and belonging

VAR __rows = FILTER(ClimateResponse, ClimateResponse[wave] = 2022 && ClimateResponse[factor] = "Inclusion & Belonging" && NOT ISBLANK(ClimateResponse[score]) && ClimateResponse[score] >= 1 && ClimateResponse[score] <= 5)
VAR __n = COUNTROWS(SUMMARIZE(__rows, ClimateResponse[respondentId]))
RETURN IF(__n >= 10, DIVIDE(COALESCE(COUNTROWS(FILTER(__rows, ClimateResponse[score] >= 4)), 0), COUNTROWS(__rows)), BLANK())

Direct dependencies: None

Inclusion Favorable % Change vs Prior Wave (points)

ClimateResponse · Climate and belonging

VAR __a = [Inclusion Favorable %] VAR __b = [Inclusion Favorable % (Prior Wave)] RETURN IF(ISBLANK(__a) || ISBLANK(__b), BLANK(), 100 * (__a - __b))

Direct dependencies: Inclusion Favorable %, Inclusion Favorable % (Prior Wave)

Inclusion Gap Non-majority minus Majority (points)

ClimateResponse · Climate and belonging

VAR __a = CALCULATE([Inclusion Favorable %], KEEPFILTERS(ClimateResponse[ethnicityRollup] = "Non-majority (all other)")) VAR __b = CALCULATE([Inclusion Favorable %], KEEPFILTERS(ClimateResponse[ethnicityRollup] = "Majority (White)")) RETURN IF(ISBLANK(__a) || ISBLANK(__b), BLANK(), 100 * (__a - __b))

Direct dependencies: Inclusion Favorable %

Inclusion Gap Women minus Men (points)

ClimateResponse · Climate and belonging

VAR __a = CALCULATE([Inclusion Favorable %], KEEPFILTERS(ClimateResponse[gender] = "Female")) VAR __b = CALCULATE([Inclusion Favorable %], KEEPFILTERS(ClimateResponse[gender] = "Male")) RETURN IF(ISBLANK(__a) || ISBLANK(__b), BLANK(), 100 * (__a - __b))

Direct dependencies: Inclusion Favorable %

Intent to Stay Favorable %

ClimateResponse · Climate and belonging

VAR __rows = FILTER(ClimateResponse, ClimateResponse[wave] = 2023 && ClimateResponse[factor] = "Intent to Stay" && NOT ISBLANK(ClimateResponse[score]) && ClimateResponse[score] >= 1 && ClimateResponse[score] <= 5)
VAR __n = COUNTROWS(SUMMARIZE(__rows, ClimateResponse[respondentId]))
RETURN IF(__n >= 10, DIVIDE(COALESCE(COUNTROWS(FILTER(__rows, ClimateResponse[score] >= 4)), 0), COUNTROWS(__rows)), BLANK())

Direct dependencies: None

eNPS

ClimateResponse · Climate and belonging

VAR __a = [eNPS promoters %] VAR __b = [eNPS detractors %] RETURN IF(ISBLANK(__a) || ISBLANK(__b), BLANK(), 100 * (__a - __b))

Direct dependencies: eNPS promoters %, eNPS detractors %

eNPS (Prior Wave)

ClimateResponse · Climate and belonging

VAR __a = [eNPS promoters % (Prior Wave)] VAR __b = [eNPS detractors % (Prior Wave)] RETURN IF(ISBLANK(__a) || ISBLANK(__b), BLANK(), 100 * (__a - __b))

Direct dependencies: eNPS promoters % (Prior Wave), eNPS detractors % (Prior Wave)

eNPS Change vs Prior Wave (points)

ClimateResponse · Climate and belonging

VAR __a = [eNPS] VAR __b = [eNPS (Prior Wave)] RETURN IF(ISBLANK(__a) || ISBLANK(__b), BLANK(), 1 * (__a - __b))

Direct dependencies: eNPS, eNPS (Prior Wave)

eNPS detractors %

ClimateResponse · Climate and belonging

VAR __rows = FILTER(ClimateResponse, ClimateResponse[wave] = 2023 && ClimateResponse[factor] = "eNPS" && NOT ISBLANK(ClimateResponse[score]) && ClimateResponse[score] >= 0 && ClimateResponse[score] <= 10)
VAR __n = COUNTROWS(SUMMARIZE(__rows, ClimateResponse[respondentId]))
RETURN IF(__n >= 10, DIVIDE(COALESCE(COUNTROWS(FILTER(__rows, ClimateResponse[score] <= 6)), 0), COUNTROWS(__rows)), BLANK())

Direct dependencies: None

eNPS detractors % (Prior Wave)

ClimateResponse · Climate and belonging

VAR __rows = FILTER(ClimateResponse, ClimateResponse[wave] = 2022 && ClimateResponse[factor] = "eNPS" && NOT ISBLANK(ClimateResponse[score]) && ClimateResponse[score] >= 0 && ClimateResponse[score] <= 10)
VAR __n = COUNTROWS(SUMMARIZE(__rows, ClimateResponse[respondentId]))
RETURN IF(__n >= 10, DIVIDE(COALESCE(COUNTROWS(FILTER(__rows, ClimateResponse[score] <= 6)), 0), COUNTROWS(__rows)), BLANK())

Direct dependencies: None

eNPS promoters %

ClimateResponse · Climate and belonging

VAR __rows = FILTER(ClimateResponse, ClimateResponse[wave] = 2023 && ClimateResponse[factor] = "eNPS" && NOT ISBLANK(ClimateResponse[score]) && ClimateResponse[score] >= 0 && ClimateResponse[score] <= 10)
VAR __n = COUNTROWS(SUMMARIZE(__rows, ClimateResponse[respondentId]))
RETURN IF(__n >= 10, DIVIDE(COALESCE(COUNTROWS(FILTER(__rows, ClimateResponse[score] >= 9)), 0), COUNTROWS(__rows)), BLANK())

Direct dependencies: None

eNPS promoters % (Prior Wave)

ClimateResponse · Climate and belonging

VAR __rows = FILTER(ClimateResponse, ClimateResponse[wave] = 2022 && ClimateResponse[factor] = "eNPS" && NOT ISBLANK(ClimateResponse[score]) && ClimateResponse[score] >= 0 && ClimateResponse[score] <= 10)
VAR __n = COUNTROWS(SUMMARIZE(__rows, ClimateResponse[respondentId]))
RETURN IF(__n >= 10, DIVIDE(COALESCE(COUNTROWS(FILTER(__rows, ClimateResponse[score] >= 9)), 0), COUNTROWS(__rows)), BLANK())

Direct dependencies: None