A messy customer list cleaned up, with the duplicates merged
A messy customer list put in one format, duplicates merged and unsure matches held for a person. For service companies and offices in Jonesboro, AR.
Demonstration using synthetic data
Every record, name, address, and log entry on this page is invented for the demonstration. No client, client system, or production record is involved, and no business result is claimed.
- Author
- Tanner Rusher
- Published
- Written for
- Small offices
Who this is for
Service companies and small offices in Northeast Arkansas whose customer list has grown for years in more than one place: an old system, a spreadsheet, the billing software, and whatever the last office manager kept. If you are about to move to new software, send a mailing, or just find a customer’s history, a messy list is what stands in the way.
The problem, in your words
Some names are in capitals and some are last name first. Phone numbers are typed every way there is. The same town is spelled three ways. The Smiths on Main Street are in there four times, once as Bob and once as Robert. Cleaning it by hand is weeks of work nobody has time for, and merging the wrong two customers is worse than leaving them apart.
What happens, step by step
- The list is exported from wherever it lives today.
- Every column is put into one format: names, phone numbers, emails, street addresses and towns.
- Likely duplicates are found, including nicknames and the same household entered more than once.
- Clear duplicates are merged into one customer, keeping the history from each.
- Possible matches that could go either way are set aside for a person to decide.
- You get back one clean list, ready to load into the system you choose.
What you approve
No two customers are merged on a guess. Every uncertain pair, such as two records with the same name in the same town, waits for someone who knows your customers to say yes or no.
What the example run showed
The video is a real run on fictional data: a made-up 5,000-row customer list for a made-up heating and air company. One run put every column into a single format, merged 1,184 duplicates into 3,816 customers, and fixed 10,798 names, phones, emails and cities. Checked against an answer key, it merged no two customers that should have stayed apart, and 74 possible matches went to a person. Those numbers come from one invented list.
Setting it up in your business
This is office automation that works on the list you already have, and hands the result back to you. It starts with a 20-minute call about where your customer records live and what you want them for. If it fits, a Workflow Review maps the job, and an Automation Pilot cleans the list and sets up a way to keep it clean. I’m based in Jonesboro and can meet in person.