How We Calculate Your Numbers

Average Daily Revenue

Your total revenue from the CSV divided by the number of unique days in the data. If you upload 90 days of data showing $42,000 total, your average daily revenue is $467.

Average Ticket Size

Total revenue divided by the number of unique transactions. We define a transaction as a unique date + time combination in your POS data.

Daily Transactions

The total number of unique transactions divided by the number of days. Each unique date + time stamp counts as one transaction.

Estimated Current Order

We estimate what you're currently ordering by looking at the maximum quantity sold on any single day for each item. The reasoning: most cafes order based on their busiest day to avoid running out.

This is an estimate. Your actual ordering may be different.

Recommended Order Quantity

Your average daily sales plus a small safety cushion based on how much your sales vary day to day. This covers most days without over-ordering. For example, if you sell an average of 3.3 croissants/day but sometimes sell 5, we might recommend ordering 5.

Daily Waste (items)

The gap between estimated current order and average daily sales. If we estimate you order 9 croissants but sell 3.3 on average, that's ~5.7 items potentially unsold per day.

Monthly Waste Cost

Daily waste units multiplied by each item's ingredient cost (not menu price), multiplied by 30 days. We use your ingredient cost percentage if you've provided it, or default to 30% of menu price — the industry average for a beverage-heavy cafe. You can update this in your cost settings.

If your actual ingredient costs are higher or lower, the dollar amounts will differ.

Savings Potential

The difference between your current estimated waste cost and what it would be if you ordered our recommended quantities. We also add an estimated 6% reduction in milk waste (assuming cafes currently over-order milk by ~10% and can cut 60% of that waste).

Savings are capped at 15% of your monthly revenue to prevent unrealistic projections.

How We Detect Milk Type

We scan your item names for keywords. "Oat Milk Latte" is classified as oat milk. "Latte" without a modifier is classified as dairy. "Almond Milk Cappuccino" is almond. If your POS doesn't include the milk type in the item name, we default to dairy.

Milk Quantity Estimates

We estimate ounces per drink based on drink type: a latte uses ~10 oz of milk, a cappuccino ~6 oz, an iced latte ~12 oz. These are standard recipe amounts — your recipes may vary.

Category Detection

If your CSV has a "Category" column, we use it directly. If not, we classify items by scanning their names for keywords: "latte", "cappuccino", "cold brew" → Beverage. "Croissant", "muffin", "sandwich" → Perishable Food. This covers most cafe menus automatically.

Column Mapping

We auto-detect 40+ column name variations. "Sale Date", "Transaction Date", "Order Date" all map to our date field. "Total", "Amount", "Sale Amount", "Revenue" all map to price. If your POS uses a name we don't recognize, the upload will tell you which column is missing.

How Comparisons Work

Each time you upload, we run the full analysis independently. Then we compare the results to your previous upload and your Week 1 data (first upload). Changes are shown as absolute values and percentages.

Cumulative Savings

The "Estimated Savings Since You Started" number is a rough projection: we sum the savings potential from each week's analysis, divided by ~4.3 to convert monthly estimates to weekly. This is directional — it shows the trend, not an exact accounting of money saved.

Item Movers

We compare each item's total revenue between this upload and the last. Items with 5%+ change are shown as "movers." Revenue changes can be driven by price changes, quantity changes, or both.