3 Ways to Clean Job Title and Industry Data in Your CRM

December 8, 2016

weidert blog author

Posted by Jamie Cartwright


Two of the most important data categories in a CRM or marketing automation database are job titles and industries. These are two types of data that help marketers organize and qualify contacts by group. Industry data helps you know whether your contact is relevant or not; while job title data provides context on whether they might be a influencer in a purchase decision or a key decision-maker (or neither).

The problem is that these categories also are areas with a lot of variation. For instance, one person might call what they do "machinery manufacturing," while another might say, "equipment manufacturing;" both are legitimate, but the different ways of describing an industry sometimes prevents a CRM from identifying which industries can be grouped together and which cannot.

How to Clean Up Key Data Categories in Your CRM

Cleaning up important data categories in a CRM requires two things: a plan for the future and an approach to changing current data. The former is necessary to create the latter, so that's where we'll start.

What Should Your Industry & Job Title Categories Be Like in Your CRM / Marketing Automation Database?

If you're familiar with most CRM platforms, data comes in several different categories. Some properties might be "string" or "text" types—basically, the properties in which anything can be written. Others are "multiple choice" or "dropdown" categories, limiting the possible options. Still others could be numerical, date properties, etc.

No matter your platform, we highly recommend that industry and job title information is collected in multiple choice data formats; not text. With text properties, typos and variations can easily prevent you from sorting contacts into the right properties. Multiple choice enables you to pre-define the categories that contacts must fit into, and sort them as you wish.

You might ask, "What if a contact doesn't belong in a certain category?" The logic of using pre-defined multiple choice properties is not that contacts will be identified precisely; it's that they'll be organized to the best possible fit.

How to Clean Up Older Data to Fit into Your New Categorization Plan

If you're currently using normal text formats for your "job title" and "industry" data, then it can be a big pain to recategorize that information into multiple choice formats.

We've recategorized data for dozens of clients who'd never previously been disciplined about lead segmentation and have learned that short of building an algorithm (which usually requires a data scientist on your team), cleaning text data into multiple choice categories is a process that should be completed over time. Don't try doing it all at once.

We recommend going category by category; using automation technology (such as HubSpot, Marketo, etc.) to identify text patterns that belong in a specific multiple choice option, and recategorizing each.

It's most important to begin with your industry targets. If you've chosen precise target verticals or groups, then you know exactly which kinds of contacts to start recategorizing. From there, move on to lower priority groups of contacts, building workflows for each to recategorize their datasets.

Consider Outside Help

If you've never cleaned up your contact database before, we recommend getting outside assistance. Because of the future planning and high learning curve involved, consider getting help from a team with a high familiarity with lead generation and segmentation.

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