CRM clean-up and sales pipeline tracking
A clean pipeline and fewer forgotten leads
BeforeDuplicates and inconsistent fields
AfterDeduplicated, standardised database
- SQL
- Excel / Power Query
- Power Automate
Service
Data analysis & cleaning
Duplicates, mixed date formats, names spelled three different ways, empty cells: with dirty data, every analysis is wrong before it even starts.
Interactive demo
Before / after: a customer file
A typical CRM export. Drag the slider to compare the raw file with the cleaned one.
Before
| # | Customer | Phone | Date | City | Amount | |
|---|---|---|---|---|---|---|
| 1 | SMITH john | j.smith@gmail | 07700 900123 | 03/02/2024 | london | 1,250.00 EUR |
| 2 | Smith John | j.smith@gmail.com | +447700900123 | 2024-02-03 | London | 1250 |
| 3 | jones emma | EMMA.JONES@OUTLOOK.COM | 07700900456 | 15 March 2024 | LEEDS | empty |
| 4 | Brown Oliver | o.brown@yahoo.co.uk | 7700 900 789 | 2024/04/22 | Bristol | 890 € |
| 5 | Taylor, Grace | grace.taylor@bt.com | 07700.900.321 | 01-05-2024 | Manchester | 2 100 |
| 6 | BROWN oliver | o.brown@yahoo.co.uk | 07700 900 789 | 22/04/2024 | bristol | 890 |
After
| # | Customer | Phone | Date | City | Amount | |
|---|---|---|---|---|---|---|
| 1 | John Smith | j.smith@gmail.com | +44 7700 900123 | 03/02/2024 | London | €1,250 |
| 2 | — merged into row 1 | |||||
| 3 | Emma Jones | emma.jones@outlook.com | +44 7700 900456 | 15/03/2024 | Leeds | missing |
| 4 | Oliver Brown | o.brown@yahoo.co.uk | +44 7700 900789 | 22/04/2024 | Bristol | €890 |
| 5 | Grace Taylor | grace.taylor@bt.com | +44 7700 900321 | 01/05/2024 | Manchester | €2,100 |
| 6 | — merged into row 4 | |||||
- 2 duplicates merged
- 1 invalid email fixed
- 6 phone numbers in international format
- 4 dates standardised
- 6 names, cities and amounts standardised
- 1 missing value flagged
The cleaning is written once (Power Query, SQL or Python), then replayed automatically on every new export.
What I deliver
In practice
- A quality check of your files and CRM
- Cleaning, deduplication and standardisation (repeatable, not manual)
- Data-entry rules so the problem doesn’t come back
- Analysis: trends, customer segments, performance drivers
- A clear summary with 3 to 5 recommendations
Tools
- SQL
- Python / pandas
- Excel / Power Query
- Power BI
2 of 2 duplicates merged in the example above
1 click to re-run the cleaning
100% consistent formats
Process
How the project runs
- 1
Assessment
I measure your data quality: duplicates, gaps, inconsistencies.
- 2
Cleaning
A repeatable pipeline, re-run on every import.
- 3
Analysis
I look for the trends and levers that matter to you.
- 4
Findings
A readable summary and prioritised recommendations.
Example projects
What a project looks like
FAQ
Your questions
Do we have to start from scratch?
No. Cleaning runs on your existing files, and the rules are replayed automatically on future imports.
Do you handle large volumes?
Yes, from a few hundred rows to several million, using SQL or Python when Excel is no longer enough.
Other services
Dashboards & reporting
Clear Power BI or Excel dashboards that refresh themselves and that your team actually uses. Process automation
Repetitive tasks (reports, reminders, data entry) run on their own with n8n, Power Automate or Make. Forecasting & predictive analytics
Anticipate sales, demand or cash flow with simple models that are explained and verifiable. Express data audit
A quick review of your data and reporting, with a prioritised action plan.
Clear Power BI or Excel dashboards that refresh themselves and that your team actually uses. Process automation
Repetitive tasks (reports, reminders, data entry) run on their own with n8n, Power Automate or Make. Forecasting & predictive analytics
Anticipate sales, demand or cash flow with simple models that are explained and verifiable. Express data audit
A quick review of your data and reporting, with a prioritised action plan.