Today’s post is from Gaurav Gupta, Manager, Professional Services at InsideView. Gaurav’s obsession with clean data borders on the maniacal, and our customers love him for it.
There are many services to help you maintain the consistency and integrity of your CRM data, but the easiest method is to simply keep your data clean in the first place. Easy, right?
If you’re a totally new company starting with an empty database, it’s easy to create some rules and force your team to live by them. For everyone else, you’re already struggling with a database with hundreds or thousands, maybe millions, of records that are already dirty. That dirty data not only leads to wasted marketing efforts and wasted time for sales calling on bad leads, it undermines reporting at every level, from forecasts to staffing needs to productivity.
Assuming that you first need to clean your existing database, and we have a good suggestion for you, we’d like to give you some suggestions on how to keep it clean going forward. And, just like brushing your teeth for good oral hygiene, good data hygiene requires constant effort. Maintaining your CRM data is an ongoing process.
Lather, Rinse, Repeat
At InsideView, we’ve worked with thousands of companies to help better inform their sales and marketing efforts. The basis of those efforts, however, relies upon the customer’s CRM data. Clean data with high integrity always leads to better results down the line, regardless of what you’re trying to accomplish.
The biggest issue we see, by far, is dirty data. To be more specific, the biggest single issue we see is duplicate data. Whether accounts, contacts, or some other element, duplicates are the main driver of errors and inconsistencies.
Sure, there are ways to “fix” duplicates, both manual and automatic, and both have their pros and cons. Automatic deduplication is faster and less-expensive, but you have little control over how individual records are edited. Manual deduplication is slow and expensive, but offers complete control at the individual record level.
Of course, avoiding duplication in the first place is the most obvious remedy. Garbage in, garbage out, right?
Hygiene Tips for Cleaner Data
Based on the thousands of customers (and tens of millions of records) we’ve worked with, here are recommendations from our Professional Services team to keep your CRM data clean. If you take these actions occasionally (at least once per quarter), your data will be cleaner, your reports will be more accurate, and your sales and marketing teams will be more productive.
- Special Characters: Check all of the input fields which include special characters, such as *,/()$#@, and correct them. For example, a company field containing “Vandelay Industries, Inc. / General Machining Division” is probably incorrect. These special characters can be a quick sign of errors.
- Company Fields with City/State/Country: Check for the company names which have either city/state/country as a part of them, and trim the names accordingly to display only the official company name. For example, changing “Dell Inc. Dallas” to simply “Dell Inc.”
- State and Country: Standardize all the values for state and country, so that “US” and “USA” and “United States of America” all become “United States.”
- Character Codes: Remove character codes, such as “&,” which were system-generated during imports and exports.
- Generic/Individual Names: Identify the company names that are generic terms or geographic names or otherwise clear signs of inappropriate placement. Generic companies, such as “Marketing Solutions,” “San Francisco,” or “Small Business,” are all obvious, but frequently cause errors.
- DBA, Formerly, AKA Names: Check for company names which includes two names separated by dba, aka, or formerly. Consider one of the names to be the current one, decide, and delete the rest.
- Duplicates (the easy ones): Identify duplicate company names by exporting the entire dataset, sort on company name, and then eliminate or merge the duplicate records. While fairly manual, you’ll be surprised at how quickly this can locate duplicates.
- Non-standard and Auto-populate Values: Eliminate all records with junk or auto-populate data that does not relate to company information, such as “Blank,” “Not Provided,” or “Do Not Use.”
Cleaner Data Starts Today
Request a free data assessment to check the health of your CRM data and determine how clean or dirty your data is.
Cleaning a dirty database is difficult, but can lead to immense improvements in sales and marketing productivity and effectiveness, as well as in reporting and forecasting accuracy. Tools do exist to help, but the moment that cleaning is completed, your database starts to get dirty again. Taking a few steps periodically can help you to maintain the high levels of data integrity needed for you, your management, and your front line sales and marketing teams to trust your CRM data.