TL;DR
- Five measures cover the ordering moment: completion, accuracy, abandonment, greet time and window time. Write down how each one is defined and counted before anyone compares a number.
- Hi Auto defines completion as the percentage of orders sent to the POS without staff intervention, and accuracy as the percentage of orders sent to the POS with every item and customization exactly right. The two always belong together.
- Hi Auto processes 100M+ orders per year, with 93%+ completion and 96% accuracy across more than 1,000 live stores.
- Read every KPI per store, per daypart and against that store’s own baseline. Data appears on the Hi Auto dashboard within a day.
Your data team opens a vendor export. One column says completion, another says greet time, a third says window time. The operations team reads the same words and expects different numbers.
That mismatch is common, and it is easy to avoid. Drive-thru KPIs only work when everyone counts the same thing in the same way. If you own the data stack at a chain with hundreds of locations, you will often be the person asked to settle what each label means.
This guide defines five measures: completion, accuracy, abandonment, greet time and window time. For each one it shows what counts, what does not, and what to write down before you compare a vendor, a store or a week. Hi Auto’s own definitions of completion and accuracy are stated exactly.
Why every KPI needs a written definition
Two reports can use the same label and measure different things. A completion rate can count every car that reached the speaker, or only the orders a system chose to attempt. A greet time can start when a car trips a sensor, or when the first word is spoken.
Neither is wrong. They are different measurements, and the gap between them can be large. Without a written definition, a number only tells you how a vendor chose to count.
A short definition card for each KPI fixes that. It records where the clock or count starts, where it ends, what is included, what is left out and which system supplies the data. The sections below fill in the first four for each measure.
Completion rate: what counts and what does not
Completion rate is the percentage of orders sent to the POS without staff intervention. That is the definition Hi Auto uses.
- What counts: an order that goes from the speaker to the POS with no crew member stepping in to finish it.
- What does not count: an order where a crew member had to step in.
Settle two things in writing. First, where an order attempt begins, because a car that reaches the speaker and leaves may or may not enter the count. Second, how a guest changing their mind mid-order is treated. Ask each vendor how they handle both, and compare vendors only when the rules match.
Completion on its own says little about quality. It always needs accuracy beside it.
Accuracy rate: the other half of the pair
Accuracy rate, also called order accuracy, is the percentage of orders sent to the POS with every item and customization exactly right.
- What counts: an order where every item, size and modifier matches what the guest asked for.
- What does not count: an order with one wrong item, or the right item with a missing or wrong customization. Mostly right is not accurate under this definition.
Read it with completion. A system can finish many orders and get some of them wrong, or be very accurate on the few orders it finishes. Each number alone flatters the system.
Hi Auto processes 100M+ orders per year and reports 93%+ completion and 96% accuracy together, as an average across more than 1,000 live stores, not a best-location result.
Abandonment rate: the guests completion can miss
Abandonment rate is the share of guests who leave before their order is finished.
- What counts: a guest who reaches the order point, starts ordering and drives away before the order is done.
- What does not count: a car that never stops at the order point. Decide how to treat a guest who leaves before saying anything, because where an order attempt begins is a choice.
Completion only counts orders that got far enough to be counted. Abandonment shows the guests who did not. Ask any vendor whether it reports abandonment and where it starts counting.
Greet time: when the clock starts
Greet time is how long a guest waits at the order point before hearing a voice.
- What counts: the time from a car arriving at the order point to the first spoken greeting.
- What does not count: time spent waiting in line behind other cars, or anything after the greeting.
The start of the clock is the part to pin down. A loop sensor, a camera and an audio trigger can each start it at a slightly different moment, so two sites can report different greet times for the same lane. Write down which trigger each store uses.
Window time: only part of the story belongs to the ordering system
Window time is how long a car spends at the window, from arrival to departure with the order.
- What counts: payment and handoff at the window.
- What does not count: time spent ordering at the speaker, or waiting in the lane before the window.
Window time depends on the kitchen and the window crew as well as on how the order was taken. Treat it as a store-level measure you track yourself, and avoid crediting or blaming the ordering system alone when it moves.
Read every KPI per store
A chain-wide average shows that something moved. It does not show where. Read each KPI per store, per daypart and, at two-lane sites, per lane, and compare each store with its own baseline from before deployment. Lane-level analytics makes that possible.
Keep every figure per store. A quiet suburban store and a busy highway store start from different places, so do not add stores together or multiply one store’s result across your store count.
Context changes how a number reads. Hi Auto’s dashboard includes insights on early closes, delivery-only periods, missed orders and other store-level nuances, so a short shift is not mistaken for a drop in performance. Data appears on the Hi Auto dashboard within a day, which gives you a fresh set of store data to check each definition against.
Hi Auto reports four KPIs: completion, accuracy, average ticket and upsell consistency. Average ticket is read per store against that store’s own pre-deployment baseline, and Hi Auto sees a lift of about 1.5% per store, within a 1-2% range.
If you run operations across many locations, our guide to comparing stores on a multi-unit dashboard picks up where the definitions end.
A definition card to take to any vendor
Fill in one card per KPI with every vendor, and compare vendors only when the cards match.
- Start and end. Where does the clock or count begin and finish, and what triggers each?
- Included. Which orders, cars or visits are in the number?
- Left out. Which are excluded, and why?
- Sample. Is it an average across all live stores, or a best location or a pilot?
- Source. Which system supplies the data, and how does it connect to your POS and BI tools? Ask about the specific POS integration method, not only whether one exists.
- Level. Can you see it per store, per daypart and per lane, and can you export order-level records?
Data ownership belongs on the card too. Ask who owns the data and what happens to the history if the contract ends, and get the answer in writing.
Agree the definitions first, then compare the numbers
Five measures, each with a written start, end, inclusion and exclusion. Completion and accuracy always together, read per store against its own baseline. That gives IT, operations and finance one set of numbers to work from instead of three.
If you would like to see how the Hi Auto product reports them, the team can walk you through it.
Next step: use the Buyer’s Guide evaluation matrix
Read the Buyer’s Guide evaluation matrix to compare providers on reporting depth, data ownership and POS integration. Then talk to the Hi Auto team about how these definitions apply across your chain.
