One job at a time.
Each of these starts the same analysis, pointed at a particular kind of file and a particular kind of question.
- Jobs
- 19
- Grouped by
- what you have
- Analysis behind them
- one
Most asked for
- Turn Excel into a dashboard.Drop in a spreadsheet and get back an interactive dashboard, built from what your data actually shows rather than from a template.
- Analyze a bank statement.A year of transactions sorted into what they actually were — with money moved between your own accounts kept out of your income, which is the mistake almost every self-built version makes.
- Get the tables out of a PDF.Tables out of a report and into a spreadsheet, with every number checked against the totals printed next to it.
A spreadsheet or a file
The general jobs. Start here if what you have is a workbook and a question.
Turn Excel into a dashboard.
Drop in a spreadsheet and get back an interactive dashboard, built from what your data actually shows rather than from a template.
Get the tables out of a PDF.
Tables out of a report and into a spreadsheet, with every number checked against the totals printed next to it.
Analyze an Excel file.
Get the findings, ranked by how much they should change what you do — each one with the calculation and the source rows behind it.
Clean a messy spreadsheet.
Duplicates, four date formats, seven spellings of the same city, numbers stored as text. Get a clean copy and a list of exactly what was changed.
Show 2 more in a spreadsheet or a fileShow fewer
Compare two spreadsheets.
Structural, value and formula changes between two versions of the same file — matched on identity, so re-sorting a sheet does not report every row as changed.
Analyze survey responses.
Likert scales, multiple choice and free text analysed together — including the question every survey tool skips, which is who did not answer.
Money in and out
Bank exports and invoices, for anyone who bills and waits to be paid.
Analyze a bank statement.
A year of transactions sorted into what they actually were — with money moved between your own accounts kept out of your income, which is the mistake almost every self-built version makes.
See when you run out of cash.
A forecast built on when your clients actually pay rather than when they agreed to — and a runway figure that leaves out deposits and tax you are holding for other people.
Find out where the ad budget went.
What each channel actually cost, once the conversions every platform claims are reconciled against the orders you actually took.
Analyze a rent roll.
Aged arrears, the tenants who are current but always late, and the month your lease expiries stack up.
People and records
Supporter, customer and member files, where the same person is often in there twice.
Find the same person entered twice.
Not identical rows — the same person recorded twice with a different email, a married name or a flat number. Every match arrives with its evidence and a decision you can reverse.
Analyze timesheets and shifts.
Where the overtime actually concentrates, which shifts are habitually mis-staffed, and what the difference costs.
Work out who is leaving, and from where.
Attrition computed one way, stated plainly, and split by the things that move it: tenure, team and who someone reported to.
Work out who stopped giving.
Retention counted by people rather than by records — which in the worked example moves it from 47% to 58% and removes 312 names from the win-back appeal.
Stock and margin
What you hold, what it cost, and which of it is actually making money.
Find the stock that is not moving.
Stock aged by when it genuinely last sold, the cash locked inside what will not move, and the products that ran out while that cash was unavailable.
Work out which products actually make money.
Contribution per unit after discounts, channel fees, payment fees, shipping, pick-pack and returns — the number that decides whether a product can afford to be advertised.
Work out which dishes make money.
Contribution per dish once modifiers, waste and how often it actually sells are all in the same calculation.
Booked time
Appointment books from clinics, salons, garages and anyone else selling slots.
Find out why people don't turn up.
A no-show rate that counts one thing rather than three, and the variables inside your own booking data that predict it — lead time, waiting time and reminder timing.
Turn a booking log into a dashboard.
Utilisation, attendance and demand by session, day and person — measured against the slots you actually offered, which is the difference between a real number and a flattering one.
More are coming, one at a time. Each needs its own demo data and its own worked example before it goes up, because a page that cannot show you the job being done is not worth reading.
3 tools are highlighted at the top of this page and also listed in their sections below.