Templates & tools
A sales spreadsheet that shows who sold — and who converted.
Most stores track one number: yesterday's revenue. But revenue is the end of the story, not the story — it can rise because traffic rose, while conversion quietly falls and the team sells less to more people. This spreadsheet logs the day per store and per seller, so revenue always arrives with the three rates that explain it: conversion, units per transaction and average ticket.
The full structure is right here on the page, free to copy into Excel or Google Sheets, with example rows and the weekly summary already thought through. If you want the formatted version for your team, we'll email you the PDF. Either way, the value is in the habit: five minutes at closing, every day, same method — the spreadsheet only pays off as a series.
The spreadsheet — columns, daily log and weekly summary
Daily log — example rows
One row per seller per day. Visitors come from the people counter (or a consistent estimate) split across the sellers on shift; the three rate columns are formulas, not typed numbers.
| Date | Store | Seller | Visitors | Sales (#) | Conversion % | Items | UPT | Revenue | Avg ticket |
|---|---|---|---|---|---|---|---|---|---|
| 07/14 | Downtown | Ana | 62 | 17 | 27.4% | 31 | 1.8 | $1,479 | $87 |
| 07/14 | Downtown | Bruno | 58 | 11 | 19.0% | 17 | 1.5 | $902 | $82 |
| 07/14 | Downtown | Carla | 47 | 15 | 31.9% | 33 | 2.2 | $1,590 | $106 |
| 07/14 | Mall | Diana | 74 | 19 | 25.7% | 40 | 2.1 | $2,052 | $108 |
| 07/14 | Mall | Eric | 69 | 12 | 17.4% | 19 | 1.6 | $1,068 | $89 |
| 07/14 | Mall | Fabi | 55 | 16 | 29.1% | 35 | 2.2 | $1,712 | $107 |
Weekly summary — what to compute
Every Monday, compute these from the week's rows. The formulas live in the sheet once and update themselves — nobody should be doing this math by hand.
| Date | Store | Seller | Visitors | Sales (#) | Conversion % | Items | UPT | Revenue | Avg ticket |
|---|---|---|---|---|---|---|---|---|---|
| Conversion % | = Sales ÷ Visitors. The share of people who walked in and bought — the first number to read, before revenue. | — | — | — | — | — | — | — | — |
| UPT (units per transaction) | = Items ÷ Sales. How much each closed sale carried; the fastest lever a seller controls. | — | — | — | — | — | — | — | — |
| Average ticket | = Revenue ÷ Sales. Read it together with UPT to see whether tickets grew by items or by price mix. | — | — | — | — | — | — | — | — |
| Seller vs. store average | Each seller's conversion and UPT against the store's weekly average — the gap is the coaching agenda, not a ranking to punish. | — | — | — | — | — | — | — | — |
| Same-store, week over week | Each store compared with itself last week, same days. Never compare a mall store to a street store — compare each one to its own series. | — | — | — | — | — | — | — | — |
Get the ready-to-use PDF
The same spreadsheet — columns, example rows and weekly formulas — formatted to print and hand to your team, delivered to your inbox.
How to use this spreadsheet
- Log at closing, every day, in five minutes. Discipline beats perfection: a rough number entered daily is worth more than a precise number reconstructed at month-end.
- Get Visitors from the people counter if you have one; if not, use a consistent estimate (e.g., a manual clicker for the same two hours daily, extrapolated). Whatever the method, never change it mid-series — a method change reads as a fake trend.
- Read by seller, not just by store. The store average hides the problem: a store with 1.8 UPT can be half the team at 2.4 and half at 1.2 — and only the per-seller rows show which conversations need coaching. This is a development tool, not a scoreboard for punishment.
- Hold a 15-minute weekly meeting on the spreadsheet: one number that moved, one action for the week. A spreadsheet that never reaches a conversation with the team is just typing.
- Set targets from your own historical series — last four weeks, same store, same weekday mix — never from a magic number. A target the series has never been near teaches the team to ignore targets.
The mistakes that kill a sales spreadsheet
A spreadsheet only the manager sees. If the team never sees its own numbers, the log becomes inspection instead of feedback — and sellers can't improve a conversion rate they don't know they have. Print the store's weekly summary, review it together, and let each seller track their own line.
Tracking only revenue. Revenue hides whether the store converted or just had a good traffic day: $10,000 on 400 visitors and $10,000 on 700 visitors are opposite stories. The rate columns — conversion, UPT, average ticket — are what tell you if the selling actually improved.
Pausing the log in a busy week. The week you skip 'because it was crazy' is usually the most informative one — and a broken series loses the week-over-week comparison that gives every other number its meaning. If time is short, log fewer columns, never fewer days.
The spreadsheet shows the numbers. We show the conversations behind them.
When a seller's conversion drops, the spreadsheet points at who — never at why. Was the greeting rushed? Did objections go unanswered? Did the add-on offer simply never happen? Cognifyze captures the in-person sales interaction itself, with consent and without identifying any individual shopper, and answers the why behind every rate this spreadsheet can only report.
In measured deployments, making the interaction visible moved same-store conversion from 51.5% to 79.5% (+28pp, p<0.001), with 383% ROI and payback in 1.4 months.
See the conversations behind the numbers — book an executive diagnostic.
Related tools and guides
Sales tracking — frequently asked questions
What should I track per day in a store?
Six raw numbers per seller — visitors, sales, items, revenue — and the three rates they produce: conversion, UPT and average ticket. That set answers the daily question that matters: did we sell more because more people came in, or because we sold better to the people who came?
How do I calculate conversion without a people counter?
Use a consistent estimate: a manual clicker during the same fixed windows every day (say, 11am–1pm and 5pm–7pm), extrapolated to the day. The absolute number will be imperfect, but the trend will be real — as long as you never change the method mid-series.
Should the spreadsheet be per store or per seller?
Per seller, rolling up to per store — one row per seller per day, with the store totals computed from the rows. Store-level numbers tell you where to look; seller-level numbers tell you what to work on in coaching. You need both, and the seller rows give you the store view for free.
Excel or Google Sheets?
For a single store, whichever the manager already opens daily. For two or more stores, Google Sheets wins: everyone logs into the same file, the summary updates in real time, and you can protect the formula columns so only the raw-entry cells get edited.
How often should I revise targets?
Monthly, against your own series — the last four weeks of the same store, respecting weekday mix and seasonality. Weekly revisions turn targets into noise; yearly ones let them drift from reality. And a target should stretch the series, not ignore it.