Coffee Shop Analytics: How to Compare Locations and Manage a Chain
Contents
- What coffee shop analytics is and what decisions it supports
- How to fairly compare coffee shops in Almaty and Astana
- What LFL is and how to separate chain growth from location growth
- How to plan shifts based on hourly revenue
- Why the average check grows and how to check upsells
- How to control milk and coffee bean write-offs and portion sizes
- How to measure coffee shop service speed
- How to evaluate a new coffee shop reaching steady operations
- Frequently asked questions
Suppose one coffee shop sells more than the others, while the manager of another asks for an extra barista. A new location increases overall turnover, but you do not yet understand what is happening with the existing ones. Coffee shop analytics helps separate these questions and evaluate each location's performance on comparable data.
Let us examine how to compare coffee shops in Almaty and Astana, evaluate shifts and upsells, and check milk and coffee bean consumption. You will get a framework for working with metrics and a fictional chain example. Start with definitions that managers can apply consistently in their daily reports.
In brief:
- Compare identical periods, operating hours, formats, and sales mix.
- Track the growth of existing locations separately from revenue from new openings.
- Analyze the average check alongside transaction count, discounts, and order composition.
- Reconcile milk and coffee bean usage against recipe cards, inventory balances, and write-off records.
- Review shift schedules by the hour, and track service speed by order events.
- For a new coffee shop, agree on a dedicated plan to reach steady operations.
What coffee shop analytics is and what decisions it supports
Coffee shop analytics means managing sales, ingredient consumption, and location processes according to agreed rules. Based on the results, you decide how to adjust purchasing, menu assortment, staff scheduling, or customer service. Every metric must have a source, an owner, and a clear action when deviations occur.
Start with the manager's question. If a line forms in the morning, you need orders by time, shift staffing, and available preparation timestamps. When milk consumption increases, you will need beverage sales, recipe cards, inventory balances, and write-offs. Total revenue helps you notice a change, while details let you choose the right action.
The general framework of sources and metrics is outlined in our restaurant analytics guide. For a coffee chain, add unified directories of locations, cup sizes, and ingredient substitutions. Record who approves new items and the effective date for modified recipes.
How to fairly compare coffee shops in Almaty and Astana
First, describe the conditions of each location. Specify format, seating, operating hours, opening date, and available sales channels. Keep separate records of rent and team composition. Comparing revenue does not explain what result remains after expenses or what resources the manager controls.
Group comparable coffee shops together. Keep the entire chain for aggregate performance, but select similar conditions when evaluating managers. Analyze a dine-in coffee shop and a takeaway spot within their respective formats. Verify specific address characteristics in Almaty or Astana using your own data, without making assumptions about the city as a whole.
| What you compare | Unified rule | What to show alongside |
|---|---|---|
| Revenue | A single set of sales net of agreed discounts and refunds | Operating days, hours, and channels |
| Average order value | Revenue / number of corresponding receipts | Order composition and transaction count |
| Workload | Receipts and items across identical hourly intervals | Actual shift staffing |
| Ingredient consumption | Identical units, periods, and accounting boundaries | Recipe cards, write-off records, and inventory status |
| Location performance | Income minus the agreed set of expenses | Rent, labor costs, and overhead expenses |
Maintain a unified calculation. Choose a calendar or operational day, and determine how to process refunds and split checks. If a shift ends after midnight, check the dates in your data exports. Display rule changes next to the totals so the manager can explain last week's recalculation.
Reconcile Kaspi receipts with POS sales separately. For the average check metric, take the total amount and transaction count from a single agreed set of transactions. Use 2GIS data when analyzing customer acquisition and reviews, if available. Confirm any link between listing views and purchases using separate data.
Verify data completeness before compiling rankings. A missing day or an unmapped item can change a coffee shop's ranking in the table. Display the upload date, open documents, and limitations next to the total. Agree on which results are ready to discuss with the manager. After correcting a source, retain the previous value and an explanation of the recalculation. That way, meeting participants can understand why a figure changed between two report reviews.
What LFL is and how to separate chain growth from location growth
LFL, or like-for-like, is a comparison of sales from comparable operating locations across two periods. Lock in a single group of coffee shops that operated in both periods. Agree on day matching and handling closures. Save this list next to the calculation.
LFL = (current period revenue of comparable locations / previous period revenue of the same locations minus 1) × 100%.
Suppose a hypothetical chain has four coffee shops: two in Almaty and two in Astana. All names and numbers below are fictional and rounded for illustration. We take two comparable periods of equal length. The existing locations operated without interruption, while the new one opened by the start of the second period.
| Hypothetical location | Previous period, tenge | Current period, tenge | Included in LFL |
|---|---|---|---|
| Almaty A | 1 000 000 | 1 200 000 | Yes |
| Almaty B | 1 000 000 | 800 000 | Yes |
| Astana A | 1 000 000 | 1 000 000 | Yes |
| Astana B, new | Did not operate | 1 000 000 | No |
Chain revenue grew from 3 000 000 to 4 000 000 tenge due to the new opening. Comparable locations together maintained 3 000 000 tenge, so LFL is zero. At the same time, Almaty A grew and Almaty B declined. The aggregate total of operating coffee shops hides opposing changes.
Analyze each deviation. For Almaty A, check transaction count, price changes, and order composition. For Almaty B, look at the same metrics and menu availability. A zero LFL does not mean that the performance of all managers remained unchanged. Evaluate the new Astana B under a separate launch plan.
How to plan shifts based on hourly revenue
Collect hourly data on revenue, receipt count, and the number of items sold. Separate weekdays and weekends, and note promotions and schedule changes. An hour with large orders and an hour with numerous quick transactions will require different workload analysis.
Compare sales against actual hours worked. Supplement planned schedules with shift covers, breaks, and duties. Factor in prep, cleaning, and receiving inventory. If an employee performed these tasks, evaluating their shift contribution by revenue alone is difficult.
In iiko, OLAP reports are available via the iikoServer API, including hourly revenue and receipt counts. Check the field list, methods, and permissions for your version in the official documentation. For r_keeper, request a sample of available data exports as well. Reconcile the closed day before automatically consolidating locations.
Calculate revenue per labor hour using a consistent definition of hours. Use this metric to review workload. When changing the schedule, agree on which hours and duties were affected. Then compare sales, line length, and prep completion across comparable days.
Why the average check grows and how to verify upsells
The average check is revenue divided by the number of matching receipts. For a chain, divide total revenue by the total number of receipts. A simple average across store metrics yields a different result if transaction counts vary. Document the formula in the general report description.
Break down changes in the average check. Check prices, discounts, item counts, and purchase composition. Review drinks, food, and channels separately. If orders contain more food, find out what happened to contribution margin after food cost. Product mix evaluation rules are covered in the article on menu ABC analysis.
Choose a verifiable definition for upsells. For example, use the share of drink receipts that also include pastries. Count these receipts in the numerator and all qualifying drink receipts in the denominator. Clarify how combos, refunds, and out-of-stock menu items are handled.
Compare results across shifts with identical pastry availability and promotional conditions. Check transaction counts and discounts. If an employee offered an add-on item as part of a promotion, track that promotion separately. You cannot determine upsell quality from average check growth without these details.
How to control milk and coffee bean write-offs and portion sizes
Start with product quantities. Calculate theoretical milk and coffee bean usage based on drinks sold and active recipe cards. Factor in cup size, extra shots, and milk substitutions if that data is available in the source. Note any limitations when data is incomplete.
Determine actual usage from starting inventory, deliveries, transfers, and the ending physical count. Convert packages to liters or kilograms using an agreed rule. Check open containers and all storage areas. Calculate sums in tenge only after verifying physical quantities.
Explain non-sale deductions. Collect write-off logs for spoilage, calibration waste, recipe testing, and staff meals. Actual usage calculated from inventory already includes these quantities. When comparing against standard consumption, show them separately, then determine the unexplained variance. The detailed formula is available in the article on food cost.
If you find a variance, check portions with the person in charge of preparation. Compare actual dose and yield against the recipe, and record the results of a test preparation. For coffee beans, break down recorded calibration waste; for milk, examine the reasons for leftovers and spoilage. Do not establish a blanket loss tolerance without auditing your own process.
Before taking inventory, close tabs, post delivery invoices, and process write-offs. Mark any variance from unposted documents as preliminary. Unexplained waste requires an audit of both bookkeeping and food preparation. Base conclusions about employee actions on verified facts.
To compare milk usage across locations, factor in product mix. Sales of black coffee and milk drinks create very different ingredient requirements. Compare actual usage against standard consumption for the specific mix sold. If recorded write-offs increase, check whether staff simply began logging existing losses on time. Discuss root causes with the manager and schedule a recount after correcting the workflow. Keep your original measurement method to verify the effect of any changes.
How to measure speed of service in a coffee shop
First, agree on the start and end points of the measurement. Measuring from order placement to handoff covers one segment. Measuring from guest arrival to drink handoff covers another. Use the receipt closing timestamp only after confirming that it corresponds to the selected milestone.
Check available timestamps. Retrieve order entry, ready, and handoff timestamps if your systems record them. If these events are not logged, conduct manual observations using an agreed procedure. Do not reconstruct prep time from hourly revenue: it only reflects sales volume during that interval.
Analyze typical wait times and extreme delays separately. Break down peak hours, order complexity, and workload. For Wolt, Glovo, and Yandex Eda delivery, separate available prep and pickup timestamps. Evaluate channel costs based on your contract, as outlined in the article on delivery margins.
After adjusting staff deployment, repeat the timing using the same method. Track returns, remakes, and complaints. Balance speed targets with beverage quality and order accuracy. To compare shifts, maintain consistent wait time definitions and observation conditions.
How to evaluate a new coffee shop reaching steady operations
Align on a phased launch plan. Track operating days, hours, team readiness, menu availability, and bookkeeping accuracy. Assign owners for deviations. Determine your ramp-up timeline based on your specific opening model, avoiding promises of a one-size-fits-all schedule for every coffee shop.
Report sales for the new location separately from LFL. Compare it against its own targets and previous comparable post-opening days. Note changes in operating hours and new menu launches. When comparing against an established coffee shop, factor in differences in format and operating stage.
Verify that the report is ready for regular review. Closed sales, clear inventory counts, and supporting documents must be in place, with the manager explaining any variances. Present rent, staff training, and other launch costs according to agreed rules. Revenue alone does not prove that a location has hit its planned targets.
Maintain a concise decision log for the new coffee shop during meetings. Record the variance, confirmed cause, action item, owner, and check-in date. If the menu is not fully available, evaluate supply deliveries and staff training before assessing demand. If operating hours change, recalculate the baseline period. At the end of each phase, document which milestones were met, what still needs work, and which source records support your conclusion.
Frequently asked questions
Is coffee shop analytics just a revenue report?
It also covers checking order composition, workload, ingredient consumption, and customer service. Choose metrics to support the manager's decisions. For each number, determine the source and the action that will follow the review.
Can you include a new coffee shop in LFL?
Include a location once it meets the agreed comparison conditions for both periods. Until then, display its sales and target performance separately. Lock in the composition of the LFL group so you can reproduce the calculation.
What should a coffee shop's average check be?
Set your target based on your menu, format, and expenses. Compare transaction count, order composition, and contribution margin after ingredient costs. A single benchmark for coffee shops in Almaty and Astana will not explain the economics of each location.
How do you tell if a barista is using too much milk?
Reconcile standard recipes, actual usage, and write-off records for the same period. Check units of measurement, inventory balances, substitutions, and preparation methods. Only then should you analyze the shift and specific events. Any overall discrepancy at a location must be explained by source data.
Does a chain of several coffee shops need Power BI?
Check whether you can regularly get comparable answers from your POS and spreadsheets. Consider a dedicated BI system when you repeatedly need to merge data sources. First, agree on the formulas, owners, and update schedule.
