Inaccurate Orders Reporting Update

We've upgraded reporting across Serve to give you more visibility into what's happening in your kitchen - and more tools to act on it. This article walks through the three key additions and how to get the most out of each one.

When a customer reports a missing or incorrect item on their order, that order is now flagged and visible in your reporting. You can click directly into the order to see:

The specific item flagged as missing, the full order contents, and the exact collection time - so you can trace the issue back to a moment in service.

Go to Reports → Performance in Serve and tap into the Orders with missing or incorrect items metric. You'll see a list of flagged orders with timestamps. Tap any order to view the full detail.

Spot patterns quickly. A single missed item could be a one-off. The same item appearing across multiple orders on the same shift is a process problem. Use the order list to check whether issues are clustering around a particular time of day, team member, or product.

Connect the order to the shift. Cross-reference the timestamp with your rota. If most flagged orders fall between 7–9pm on a Friday, that's a busy service problem - not a menu problem.

Use it in team reviews. Rather than having abstract conversations about quality, bring the actual order list to your next kitchen briefing. It makes the issue real and removes ambiguity.

Tip: Don't wait for a spike to check this. A quick weekly scan - even 5 minutes - will catch issues before they compound into a rating drop.

A dedicated report page that gives you a full breakdown of missing and inaccurate item performance, including:

Your missing item rate broken down by day of the week, most frequently missed products, and brand. Click any flagged order on the left to see the full detail.

Go to Reports → Performance and tap Orders with missing or incorrect items. The full breakdown loads below the headline metric.

Start with the top missed items list. This is the most actionable part of the report. If "Burger Meal Deal for 2" appears 11 times in a period, the question to ask is: is this a packing issue, a prep issue, or a menu configuration issue? Each has a different fix.

Common causes by item type:

Use the day-of-week chart to identify service pressure points. Days showing above 2% (flagged in red) are where your kitchen is most under pressure. Use this to decide where to add extra checks - for example, a dedicated packing role on Friday and Saturday evenings.

Track your rate week-on-week. A falling rate means your fixes are working. A rising rate is an early warning sign. The comparison to your previous period (shown in green or red at the top) gives you the headline at a glance.

Tip: Aim to keep your missing item rate below 2%, which allows for the reality that not every flagged order is a kitchen error. If you're consistently above 2%, that's the signal to dig in here before looking at other metrics.

Performance metrics across Serve - including rider wait time - now break down by individual brand. If you run SoBe Burger and another brand out of the same kitchen, you can see each brand's performance separately rather than a single blended number.

Your key kitchen metrics at a glance. Rider wait time now expands to show a per-brand breakdown.

Go to Reports → Performance and tap on any metric that shows a brand breakdown (e.g. Rider wait time). The breakdown by brand appears in an expanded panel below the headline figure.

Identify which brand is driving a metric. If your overall rider wait time is high, the brand breakdown tells you where the bottleneck actually is. A blended 8% could mean both brands are at 8% - or it could mean one is at 4% and one is at 12%. The fix is very different in each case.

Prioritise your kitchen workflow accordingly. If one brand consistently shows higher rider wait time, consider whether its prep times, ticket prioritisation, or workstation setup needs reviewing. Busy Mode settings should also be utilised to manage order flow.

Benchmark brands against each other. Over time, you'll start to see which of your brands runs more cleanly operationally. Use this to inform how you organise your kitchen - ticket prioritisation, packing station setup, and how you allocate staff attention across brands during busy service.

Tip: Rider wait time is one of the metrics delivery platforms use to assess kitchen performance. Keeping it below 7% across all brands protects your platform standing and reduces the chance of being deprioritised in search results.

The Average Orders chart now lets you toggle between three views - Week, Day, and Hour - so you can look at your order volume at whatever level of detail is useful. You can also switch on a comparison against your previous period to see whether performance is up or down.

Your sales over time (top) and average orders by day of week (bottom). Toggle between Week, Day, and Hour to change the level of detail, and switch on the comparison to see how your current period tracks against the previous one.

Go to Reports → Overview in Serve. The Average Orders chart appears below your sales graph. Use the Week / Day / Hour tabs to switch views, and toggle Compare with past performance to layer in your previous period.

Use the Hour view to find your peak windows. Knowing exactly which hours drive the most orders helps you plan staffing, prep, and kitchen capacity more precisely - rather than working off a general sense of when it gets busy.

Use the Day view to spot your strongest and weakest days. If Thursday is consistently underperforming against the previous period, that's worth investigating. Is it a local factor, a menu issue, or a platform visibility problem?

Use the comparison toggle to track progress. Toggling on the previous period gives you a quick read on whether changes you've made — new menu items, adjusted hours, promotional activity - are moving the numbers.

Tip: Check the Hour view before planning any extended hours for the World Cup fixtures. It'll show you exactly when orders tail off in the evening and help you decide whether staying open later is worth it for your location.

If you have questions about your reporting or want support interpreting your data, reach out to the Sessions Support team via the chat bubble.