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A lead database is no longer a rolodex. Lead data doesn’t come from standard business cards. Lead sources are plenty and highly varried whether it’s from social media, free trials, whitepaper downloads, landing pages, custom built lead lists, webinar registrations or other sources. Lots of lead sources can mean a lot of leads for your marketing database which is great news! The bad news is lots of lead data sources can also mean a lot of different formats, a lot of different data points and a lot of inconsistency in your database records. That is something you need to avoid.

The cleaner and more consistent your marketing lead data is, the more effective it will be in generating consistent “sales ready” leads whether you use a lead nurturing solution, an email marketing application or just simply run individual campaigns off your data. If you imagine your lead generation sources, your lead database or CRM and nurturing machine as three parts of a larger machine connected by pipes, you can’t really do much about data being inconsistent at the lead generation level. Different sources collect different data points. A newsletter subscription form may collect nothing but a name and and email address where as a landing page form fill can collect additional details like telephone number, Job title and company name. Changing the sources is not always an option.

The pipeline between the lead generation sources and the lead database (or CRM) is the point where you can fix this problem. One of the biggest causes of inconsistent and incomplete data is allowing leads generated at different sources to flow straight into the database assuming it can be corrected or dealt with later. It almost never happens! However, this is the point at which you need to put in a data normalization process which helps fill in missing data points and standardizes your lead records before they get to your CRM. Some of the things you may want to do before they get past this stage are:

• Filter out any garbage records which will just be redundant
• Maintain a “minimum required” fields like name, company, address, email, phone number and job title which have to be filled in before considering it a completed record
• Append missing data such as email addresses, postal addresses, account information which is missing
• Check for duplication
• Format and standardize job titles, number formats, naming conventions and so on