Dirty data is old data. It is wrong data – double entered and triple entered. It is bad data. Here is an example:
ACME Corporation (record 1 - which is the only correct listing of the name)
ACME Inc (record 2 - should be Corporation, not Inc.)
A.C.M.E. (record 3 – someone just botched it as an acronym)
ACMG Corporation (record 4 – correct other than the typo)
How do you cleanse the data?
So in addition to having multiple records for the same companies (both prospects and clients), there is another big area of bad data – as follows:
Contact person is no longer with the company
Contact person got promoted
Contact person has a completely different role
Contact out on extended leave, hiatus, sabbatical, or dead (yes, it happens)
Finally there are companies in the CRM system who:
Have been acquired and now have another name
Have divided and now have more than one company name
Have closed and are out of business
Start the new year right with some guidelines or policies around how CRM records are filled out, or how spreadsheets are tracked. I’m only mentioning spreadsheets because of the rampant use of them still in mid-sized companies. That is another discussion for another day.
Sample Data Policy:
A new record must have the prospect’s first and last name, company, and e-mail address at a minimum. Add the phone number if available. Tag (categorize) the record appropriately as a pre-prospect, prospect, qualified, proposed, or client. [Use the steps and your own terminology within your sales process to help your SDRs and BDRs enter clean, verified data.]
Every time a new prospect is contacted, there needs to be a next action set. This is not so much a clean data issue but is so critical in moving your sales opportunities forward that it needs to be mentioned.
If you can reward your reps for clean, updated territory lists, that can go a long way. Knowing the integrity of your data is important. One client factors data updating in how they bonus front line reps and account managers.
There are companies that provide data updating services such as email verification, address verification, and “match and merge” services.
Bad leads slow down your sales force. You must find a way to regularly update your data, merge records, and delete bad records.
Take a look at Getting Your Data House in Order by Jake Dolezal for ideas about data governance and organization.
What can you put in place in 2015 to help sales reps be more effective in starting with the best data possible?
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