Sigmera for Small-business owners
Running a small business means being the person who exports the customer list, the person who fixes it, and the person who imports it somewhere else. There is no data team and no budget for one. Sigmera cleans that file in a browser tab — deduplicate, fix the email column, standardise phone numbers, split names — and the customer data never leaves your laptop, so there is no third-party service to vet before you start.
The data problems you hit
- The same customer exists in three systems under three spellings. The till says "J Okonkwo", the booking form captured "Joy Okonkwo", the newsletter has "joy.okonkwo@" with a different address. Excel's Remove Duplicates only removes rows where the values in the selected columns match exactly, so none of these get caught — and the deletion it does perform is permanent unless you undo it immediately.
- Every platform wants the file in a different shape. Shopify requires an Email column to be present even if the value is blank, caps the customer CSV at 15 MB, and needs UTF-8 encoding with headers on the first line. Mailchimp takes CSV or TXT and requires a column labelled Email Address. One export never satisfies both without editing.
- Duplicate rows quietly cost money and reputation. Shopify skips customers with duplicate email addresses or phone numbers on import and keeps only the last profile with that address, so the record that survives may not be the one with the current details. Mailchimp will not reimport duplicates already in an audience, which hides the problem rather than fixing it in your source file.
- Phone numbers arrive in every format a customer can type, and a booking reminder or SMS campaign needs one consistent format to dial. Fixing that column by hand across a few thousand rows is an evening you do not have.
- The contact file holds real personal data — names, emails, phone numbers, sometimes addresses — and the obvious fix is to paste it into whichever free online cleaner ranks first. That means sending your customer list to a company you have not checked, which runs against the GDPR's data-minimisation principle that personal data be "adequate, relevant and limited to what is necessary in relation to the purposes for which they are processed".
- Nobody owns the spreadsheet. It lives in someone's Downloads folder as customers-final-v3-USE-THIS.csv, and each round of manual cleanup gets thrown away because it was done on a copy that then went stale.
What you can do in Sigmera
You have merged the till export, the booking system, and the newsletter list into one file for a Shopify or CRM import. Matching on email catches most repeats, but near-matches from typing and trailing spaces slip through. Dedupe on the email column with case and whitespace ignored, so [email protected] and [email protected] collapse into one row before the importer makes that decision for you.
Sourced and hand-typed email columns carry trailing spaces, mailto: prefixes, two addresses crammed into one cell, and domain typos. Since an email column is mandatory for both Shopify and Mailchimp, a messy one is the difference between an import that finishes and one that half-fails with a report you then have to read. Lowercase, trim, and drop the invalid rows first.
Appointment reminders, delivery texts, and any dialler expect one consistent international format. Your file has 07700 900123, +44 7700 900123, and (07700) 900-123 in the same column. Convert the whole column to E.164 in one pass rather than writing a formula you will have to rewrite for the next country code.
Your booking form captured one Name field; the email platform and the invoicing software both want first and last name separately so the greeting is not "Hi Joy Okonkwo,". Split the column, check the rows with middle names and double-barrelled surnames, and export.
A platform hands you a CSV, and opening it by double-click mangles the leading zeros on order numbers and reformats the dates. Convert it to .xlsx first so the columns keep their types, or go the other way and pull a clean single-sheet CSV out of a workbook when an importer refuses anything else.
Once the file is clean you want one answer out of it — revenue by month, orders by channel, customers by city. Group by the category column, summarize the value column, and export the chart as a PNG for the accountant or the bank.
Sources
Frequently asked questions
- What is the fastest way to clean a customer list for a small business?
- Work in a fixed order and the job shrinks. Deduplicate first, because every later step then runs on fewer rows. Then fix the email column, standardise phone numbers, split the name field if the destination needs it, and export. Doing it in that order rather than fixing whatever you notice first is what turns an afternoon into twenty minutes.
- Why does Excel's Remove Duplicates miss so many of my duplicates?
- Because it requires an exact match. Microsoft's documentation is explicit that the key used to find duplicates is the combined value of the columns you selected, and the entire row is removed only when that key repeats. A trailing space, a capital letter, or a slightly different spelling makes two rows unique to Excel. It also warns that the removal permanently deletes values, so copy the sheet first.
- Do I need to clean the file before importing to Shopify?
- It helps considerably. Shopify requires the Email column to be present even when a value is blank, needs UTF-8 encoding with headers on the first line, and caps customer CSV files at 15 MB. It also skips any customer with a duplicate email address or phone number and keeps only the last profile carrying it — so if your file has repeats, Shopify picks which record survives rather than you.
- What does Mailchimp need in an import file?
- Mailchimp accepts CSV or TXT files and requires a column labelled Email Address. It matches obvious columns to fields automatically and lets you map the rest by hand. It will not reimport duplicate, bounced, or unsubscribed addresses that already exist in the audience, which keeps the audience tidy but leaves the duplicates sitting in your own file for next time.
- Is it safe to use a free online CSV cleaner on my customer list?
- It depends entirely on whether the tool uploads the file. Most online cleaners send your data to their server, process it there, and send it back — which means your customer list has been handed to a company you have not vetted, and adds a processor you would have to account for. The GDPR's data-minimisation principle asks that processing be limited to what is necessary. A tool that runs in your browser avoids the question because the file never travels.
- Do I need to install anything?
- No. Sigmera runs in the browser tab you already have open, using client-side JavaScript, so it works on a Windows laptop, a Mac, or a Chromebook without admin rights or an IT request. The workspace keeps working offline once it has loaded, because there is no server call to make.
- How much does this cost for a small business?
- There is a free tier, and previewing every tool is unlimited and free — you only consume an allowance when you export a finished file. Paid plans are priced on downloads per month rather than per seat or per row, so a business that cleans a list occasionally stays on the free tier. Current figures are on the pricing page.
- Can I clean a file that has thousands of rows?
- Yes. Processing happens on your device, so the limit is your own machine's memory rather than a row cap set by a vendor. A typical small-business customer export — a few thousand to a few tens of thousands of rows — is well inside what a normal laptop handles in a browser tab.