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Field Mapping Recommendations

The purpose of this guide is to give recommendations on the type of fields that will offer lots of value when mapped to OpenGTM. Keep in mind that on every object OpenGTM allows you to map as many other fields as you like.

Telling a Story with Data​

Data is fascinating as it can tell powerful stories. Data in business should be treated as important as revenue because data understood often unlocks troves of revenue. A CRM or customer relationship management solution can help you keep track of the data most important to your business. This data should include information about people, entities, transactions, and projects important to your business. Often businesses are already keeping track of much of this information in a CRM, but struggle to make input mechanisms simple and to know how to bring it all together in a meaningful way.

It is important to understand why you're tracking the data in the first place. You need a phone number to know how to call an interested person. You need their name to know who to ask for when someone answers the call. You need their title to have some insight into what their job entails. This journey of determining what to track must maintain a healthy balance of perspective from all parties using and especially inputing the data. We must treat the data input experience like we treat our product onboarding experiences.

A question to ask yourself as a CRM admin or RevOps professional is, if you could only keep 10 fields in the entire CRM which would they be and why? CRM fields should be prioritize as not all fields are created equally. The most important fields should be the easiest to input. Put together a team or committee that can have these types of conversations regularly. Ideally, this team or committee would determine the fields that should be synced to OpenGTM, but these teams or committees are not common. Therefore, take 10 to 20 minutes and jot down some notes about the most important parts of your buyer journey and sales process. Then identify the fields in the CRM that track the key components of these experiences. These fields and ultimately fields that help you see patterns are the fields that should be synced to OpenGTM as either standard or other fields.

Examples of Other Fields to Include​

Where a lead is really at the information gathering and vetting stage of a sales process any of these fields could be applicable to the Lead object in OpenGTM as well.

Account​

  • Region
  • Year Founded
  • Website Suffix
  • Technologies Used
  • Apollo Intent Score
  • 6Sense Intent Score

Contact​

  • Phone Number Area Code
  • Buying Role
  • Years of Experience
  • Interests
  • Number of LinkedIn Connections
  • Level of Education
  • Social Platforms Used
  • Certifications
  • Languages
  • Apollo Intent Score
  • 6Sense Intent Score

Opportunity​

  • Clari CRM Score
  • Gong Deal Likihood Score
  • Number of Sales Activities
  • Number of Marketing Activities
  • Number of Calls
  • Number of Emails
  • Number of Meetings
  • Date of First Meeting
  • First Touch Campaign
  • Last Touch Campaign
  • Active Competitors
  • Number of Products

Field Types to Include​

Select or multi-select, boolean (true or false), date, datetime, and number input types are an analyst's best friend. They offer more analytical value to business and make it easier for professionals to input data.

Fields Not to Include​

Certain types of data should typically be excluded from analytics processes due to privacy concerns or because they simply do not contribute to meaningful insights:

  • IDs: Unique identification numbers, whether for users, transactions, or products, often don't add value to the analysis and pose a risk of identifying individuals.
  • Emails: Email addresses are personally identifiable information and should be excluded to ensure privacy and comply with data protection regulations.
  • Phone Numbers: Similar to emails, phone numbers are sensitive and should be kept out of analytical processes.
  • Open Form Text Fields: These fields usually offer very little analytical value outside of using natural language processing.
  • Others with Unique Values on Every Row or Record: Any field that has a unique value for every entry (like serial numbers or unique transaction IDs) typically doesn't contribute to aggregate analysis and might risk identifying individuals or sensitive business information.