Employees
Granite Subaru · August 2025 · Salesperson view
Granite Auto Group is fictional. Operating figures are synthetic.· data and methodology
Every warehouse record in this project is synthetic. Granite Auto Group and its three stores are fictional. No real dealership, customer, employee or lending data exists anywhere in the project.
Real-engine validation pending. Neither accepted ADR-0008 path has recorded a result, so nothing on this page may be read as evidence that the Power BI semantic model has been validated. This console renders exported SQL figures; rendering a number in HTML proves nothing about a DAX measure.
Filters and controls3 applied
This role family
People credited
4Salesperson
Retail units
41Credited to these people in this period
Comparison-eligible on the leading figure
2 of 4Minimum sample 10 · 2 below it, and the ratio is withheld rather than printed
Units delivered under a salesperson credit, the gross on them, and the opportunity and mix that surrounded it.
The floor is a publication discipline, not a performance threshold: below it this page declines to print a comparative ratio and says nothing whatever about the person. It applies to each measure’s own denominator, so someone can be eligible on one figure and not on another in the same period.
The people
Credited activity, by person
Store, then role, then employee code. That order is fixed and there is no control to change it: a list sorted by a measure is a leaderboard whether or not it is labelled one. Nobody can sell inventory the store does not have or work a lead they were not assigned, so opportunity is on every row.
4 people in the Salesperson family, each with their credited volume, the measures their role is governed by, the sample behind each one, and the mix and opportunity around it.
- Front gross per retail unitInsufficient sampleSample 6 of 10 retail unitsTotal gross per retail unitInsufficient sampleSample 6 of 10 retail unitsNew and used mixNew 2 (33.3%) · Used 4 (66.7%)Assigned leads 24Commonest lead source Owned Digital (9)Certified, inside used 1Deals with a desk manager 6 of 6
- Front gross per retail unitInsufficient sampleSample 5 of 10 retail unitsTotal gross per retail unitInsufficient sampleSample 5 of 10 retail unitsNew and used mixNew 1 (20.0%) · Used 4 (80.0%)Assigned leads 27Commonest lead source Owned Digital (11)Certified, inside used 1Deals with a desk manager 5 of 5
- Front gross per retail unit$689n 12 retail unitsTotal gross per retail unit$1,410n 12 retail unitsNew and used mixNew 6 (50.0%) · Used 6 (50.0%)Assigned leads 24Commonest lead source Owned Digital (12)Certified, inside used 3Deals with a desk manager 12 of 12
- Front gross per retail unit$1,576n 18 retail unitsTotal gross per retail unit$2,737n 18 retail unitsNew and used mixNew 10 (55.6%) · Used 8 (44.4%)Assigned leads 44Commonest lead source Owned Digital (17)Certified, inside used 2Deals with a desk manager 18 of 18
What the stores had to work with
Average active units, by store
An average over the snapshot days observed in the period: a stock count summed across days overstates by roughly the number of days. Availability, not difficulty.
GSA-002 88.6 average active units over 31 observed snapshot days
GSA-00288.6
31 observed snapshot days
Store context, not an employee measure. It is not on any employee row and cannot be summed across people.
EMP-00022 in this period
An investigation surface: what was credited, the sample behind each figure, the mix around it, and where to look next. Not a personnel record.
- Sales and gross for these stores and periodThe governed store totals these deliveries are inside. Not filtered to this person: that route declares the employee parameter not applicable.
How to read these figures
The context that changes interpretation is on the rows above. What is behind this disclosure is how the arithmetic was done.
How to read employee metrics
Every ratio is a ratio of sums. Gross per retail unit is total gross divided by total retail units at the grain being reported — never an average of daily figures, per-person figures or store figures, which are different numbers and all of them wrong.
Each figure has its own sample, and its own floor verdict. Gross per unit is governed by retail units; contact rate by valid assigned leads; appointment-set rate by contacted leads, never by all valid ones; show rate by eligible appointments on the scheduled date; show-to-sale by appointments shown on the show date. One person can be comparison-eligible on one figure and not on another in the same period, and the page says so per figure.
Four absences, four statements. “Not applicable” means the measure does not belong to the role. “Insufficient sample” means it does and the denominator is below the floor. “No data” means it does and nothing was observed. A zero is a real observed value and is none of those three.
History keeps its own store and title. Every row’s role, store and tenure band are the values that were true when the activity happened, taken from the employee version the transaction points at. A transfer or a promotion later does not move earlier activity to the new store or relabel it with the new title.
Certified units are used units. The certified count shown beside a mix is a subset of the used count, not a third category, and adding it to used would double count.
Cash deals are inside the finance denominators. Reserve and back gross per retail unit divide by every retail delivery, including cash deals, which cannot generate reserve. A different cash mix moves both figures for reasons unrelated to the finance office, which is why the structure mix is drawn beside them on the row.
Appointments and leads are different populations. One lead can produce several appointments, so the lead-grain rates and the appointment-grain rates do not share a denominator, and the BDC row draws them as two separate bands for that reason. Show rate is on the scheduled date and excludes appointments cancelled in advance — an exclusion a store can game, which is why the cancellation count is on the row beside it. Show-to-sale is on the show date, so period-to-date conversion improves as the data matures.
A response time nobody answered is not a fast one. A never-responded lead has no response value at all and is excluded from the median rather than counted as zero seconds. The count of leads never answered is on the row beside the median, because the statistic is blind to them.
Lead-source mix is context, not a score. This project publishes no lead-quality ranking, difficulty index or source weighting, and none is derivable here. The mix is shown because comparing two people’s contact rates without it compares two different jobs.
Nothing here is causal. A figure is credited to a person, observed for them, or on transactions they handled. The model records associations between people and outcomes; it does not isolate an individual effect, and no figure on this page supports a statement about individual skill.
Every person on this page is invented, and so is every number. The codes identify fictional employees in a synthetic dataset. No name, contact detail, hire date, termination date, exact tenure, age, pay, commission or protected attribute exists anywhere in the governed export this page reads, and no figure here is comparable to any published market figure.