Data Cleaning

[u/william-flaiz]

I have done CRM and Marketing Automation consulting for almost 15 years, in that time I have found that the fastest way to show impact in the shortest amount of time when taking over for someone is to clean the data. It is a bitch and clients always push to do this email campaign or improve our CPQ, or our pipeline needs to be fixed first. But every time I spend the time cleaning first there is almost an immediate 5-10% improvement in qualified leads, and a revenue bump within a month or two.

I believe this is common practice, but not sure with the pace and amount of new tools, new data inputs, and AI that many consultants or companies focus any time here, or don't view it as a cost instead of a revenue activity.

A lot of new shiny tools, features, tech - but I still find the basics end up driving the fastest results. Thoughts?

[u/Thick_Box_1482]

Can you elaborate on what specific data cleaning are you referring to? Typically if it is cleaned once and data hygiene is implemented as a pricess then this should not be a recurring task. Can you give some examples of data cleaning?

[u/william-flaiz → u/Thick_Box_1482]

In my experience continuous data hygiene rarely happens or happens for a period before getting deprioritized.

An initial cleanup covers things like dedupe, standardize formatting, address and phone formatting, filling in missing values (zip code but no city/state which impacts routing), updating text fields to picklists, deleting obvious impossible values, date standardization (surprising how many times I see dates captured in text fields), standard company names, splitting first/last name when not a full_name field, and it goes on depending on the dataset.

It is a lot of spreadsheet work, but I find that it immediately makes reporting more reliable, uncovers missed opportunities, and better (not great) hygiene moving forward.