You rely on Industry field values for searching, analysis, lead scoring, and reporting, but you encounter many variations for the same industry, such as "Information Technology," "Information Services," and "IT." You need a simple way to ensure these are consistent.
Insycle offers several tools to help you standardize Industry field data. The Transform Data module is ideal for standardizing large amounts of data already in your CRM.
If you have a CSV file of new data to import, the Magical Import module can standardize values before writing them to your CRM. See Standardize Industry on Import Using the Magical Import Module below for details.
In this article, we’ll walk through an example of standardizing Industry values in records already in your CRM. We’ll use the Map: Contains Word and Map: Contains Substring functions to replace variations of the same industry, such as “Tech” and “Software,” with “Technology.” In the same operation, we’ll also replace camping and recreation values with “Outdoors,” and tutoring, training, and higher ed values with “Education.”
Map Industry Values with the Transform Data Module
Process Summary
- Filter your data down to the records you want to update.
- Map your Industry field values.
- Preview the changes, then apply them to the CRM records.
Step-by-Step Instructions
1. Filter Your Records
- Navigate to Data Management > Transform Data.
- In the top menu, select the database and object type (for example, Companies).
- Explore the templates to see whether an existing solution is close to what you need.
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Under 1. Filter Records, configure the Filter to find records whose Industry values contain the terms you plan to standardize. Separate multiple values with the pipe character ( | ), which means OR. Leave the Case Sensitive box unchecked to find capitalization variants.
For example:- Field: Industry
- Condition: contains
- Value: "tech|software|IT|camping|recreation|tutoring|training|higher ed"
The image above shows the 1. Filter Records step in the Transform Data module with the Filter tab selected. One filter row is configured: the field dropdown is set to “Industry,” the condition dropdown is set to “contains,” and the value field begins “tech|software|IT|camping|recreation|tutori” (the rest of the value is cut off). A Case Sensitive checkbox beside the value field is unchecked. Below the row are the Field and Clear buttons, a yellow Search button, and a dark blue Export button.
- Click Search. Insycle lists the matching records in the Record Viewer at the bottom of the page.
- If you change the filter, click Search again to refresh the Record Viewer.
The image above shows the Record Viewer in the Transform Data module, below the 1. Filter Records step. The table has a selection checkbox column and three columns: Company Name, Industry, and Website URL. No checkboxes are selected.
2. Map Your Industry Field Values
Under 2. Configure Changes, give Insycle instructions for the changes you want to make to the Industry field.
Using the Map functions, you can locate specific values or strings of characters in the Industry field and replace the entire value or just the matching portion.
Each function row has these settings:
- Field Name: The field that contains the value you want to modify (Industry).
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Function: The type of match to make. This example uses two Map functions, and both ignore capitalization:
- Map: Contains word matches a whole word, such as an abbreviation, that you don’t want matched inside a longer word.
- Map: Contains substring matches a string of characters anywhere in the value, including part of a longer word or a phrase.
- Existing Text: The value(s) you are looking for. If you enter several different strings, and any one of them is found, the value is replaced. The pipe “|” character (located just above the Enter key on your keyboard) separates the different strings.
- New Text: The new value that replaces the specified portion of the Industry value if the Existing Text is found.
In this example, you'll set up three function rows to standardize Industry values in one operation.
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Standardize technology values. In the first row, enter the following:
- Field Name: Industry
- Function: Map: Contains word. This function finds each term as a separate word, so “IT” matches “IT Services” but not words that merely contain the letters "it", and it matches regardless of capitalization.
- Existing Text: IT|software|tech|technology
- New Text: Technology
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Standardize camping and recreation values. Click the plus at the end of the row to add another function, then enter the following:
- Field Name: Industry
- Function: Map: Contains word. These terms are whole words in the Industry values.
- Existing Text: Camping|Recreation
- New Text: Outdoors
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Standardize tutoring, training, and higher ed values. Click the plus again, then enter the following:
- Field Name: Industry
- Function: Map: Contains substring. This function finds the text anywhere in the value, so “tutor” also matches longer words like “tutoring,” and “higher ed” matches both “Higher Ed” and “Higher Education.” Map: Contains word would not match “higher ed” inside “Higher Education.”
- Existing Text: tutor|training|higher ed
- New Text: Education
- Review the order of your functions. Order matters, so use the down arrow and up arrow buttons at the end of a row to rearrange the functions. Click the plus to add a function, or click the X to remove one.
- Optional: Export your mapping to Data Logic. When you configure a Map function, a blue cloud icon appears at the end of the row. Click it to export your mapping configuration as a Blueprint CSV file that is ready to upload to the Data Logic module. This is useful when your mapping logic has grown to a scale that is better managed continuously in Data Logic, where it can run automatically across all records and be updated at any time by editing the CSV.
The image above shows the 2. Configure Changes step in the Transform Data module. Three function rows are configured for the Industry field. In the first row, Field Name is “Industry,” Function is “Map: Contains word,” Existing Text begins “IT|software|tech|techno” (cut off), and New Text is “Technology.” In the second row, Function is “Map: Contains word,” Existing Text is “Camping|Recreation,” and New Text is “Outdoors.” In the third row, Function is “Map: Contains substring,” Existing Text is “tutor|training|higher ed,” and New Text is “Education.” A Field button appears below the rows.
3. Preview Changes and Update CRM Records
Preview Changes in the CSV Report
With the filters and functions set up, you can preview the changes in a CSV file. Verify that your formatting works as expected before pushing changes to your live database.
Under 3. Review, click Review, then select Preview in the popup.
On the Notify tab, you can select recipients for the email report and add additional context to the message. (Make sure to hit Enter after each email address.)
On the When tab, click Run Now and select which records to apply the change to (you could do All, but if you have a large number of records, you may just want to do a chunk for your preview), then click the Run Now button.
Open the CSV file from your email in a spreadsheet application and review the (Before) and (After) values for each field.
If the results don't look as you expected, return to your filters and functions and make adjustments before previewing again.
The image above shows the CSV preview report from the Transform Data module, opened in a spreadsheet application. The columns are Result, Message, ID, Company Name, Deeplink, Industry (Before), and Industry (After). The Industry (Before) column lists each record’s Industry value as it currently exists in the CRM. The Industry (After) column lists the value that results from the functions configured under 2. Configure Changes. Each row pairs the two values for one company. The visible Industry (After) values are “Technology,” “Education,” and “Outdoors.” All 13 visible rows show “Succeeded” in the Message column. Twelve rows show “Updated” in the Result column, and one row (Bosder) shows “Unmodified,” with “Technology” in both the Industry (Before) and Industry (After) columns.
Apply Changes to the CRM
If everything in your CSV preview looks correct, return to Insycle and move forward with applying the changes to the live CRM data.
Under 3. Review, click the Review button. This time, select Update mode.
On the When tab, you should use Run Now the first time you apply these changes to the CRM.
Save Templates and Set Up Automation to Maintain Formatting
After you've seen the results in the CRM and you are satisfied with how the operation runs, you can save your configuration as a template and set up automation so this mapping runs on a set schedule. By automating with a template, you'll save time and ensure your fields are consistently updated over time.
Tips for Mapping Values
If you're unsure about what variations you have, do some exploratory work to identify the non-standard values that exist.
Use the Cleanse Data module to get a summary of all data variations in a specific field in your database. This makes it easy to review the values in use and determine what needs to be cleaned up.
Learn more about using the Cleanse Data module.
Advanced How-Tos
Standardize Industry on Import Using the Magical Import Module
If you have a CSV file from an external data source, the Data Preparation step in the Magical Import module can replace inconsistent Industry values with your standardized values before the data is written to your CRM. It uses the same Map functions found in the Transform Data module. Data Preparation works only on the CSV values shown in the Preview, so it does not read or change data already in your CRM.
For detailed instructions, see Standardize Industry Values before Importing Data.
Understanding Map Functions
The Transform Data module includes several Map functions that let you locate data within your database and replace one value with another.
| Function | Description | When to Use It |
|---|---|---|
| Map: Values |
All the content in the field must match the Existing Text value. If it matches, then the entire field is overwritten with the New Text. Example: If the field is City, and Existing Text "NY|NYC|New York", New Text "NY", then "new york" becomes "NY" |
Use when the entire field value must match your Existing Text exactly, such as replacing known variations of a complete value (“NY,” “NYC,” “New York”) with one standard value. Values that only contain the text are not changed. |
| Map: Terms |
Find any standalone term in the field. If found, just the term is overwritten with the New Text. Example: If the field is Address, with Existing Text "st|st.", and New Text "Street", then "Main St." becomes "Main Street" |
Use when you want to replace only part of a value and keep the rest, such as expanding an abbreviation inside an address (“Main St.” becomes “Main Street”). |
| Map: Contains word |
Find a specific word in the field. If found, the entire field value is overwritten. Example: If the field is Industry, with Existing Text "Tech", and New Text "Technology", then "Tech Sector" becomes "Technology" |
Use to replace the entire value when it includes a specific word, without matching that text inside longer words, such as “Tech” in “Tech Sector” or short abbreviations like “IT.” The entire value is replaced. |
| Map: NOT contains word |
Inversely, look for a specific word in the field. If it is not found, the entire field value is overwritten. Example: If the field is Favorite Color, with Existing Text "Red|Yellow|Blue", and New Text "Non-Primary Color", then "Purple" becomes "Non-Primary Color" |
Use to change all values that don’t contain the specified words to one value, such as changing anything that isn’t “red|yellow|blue” to “Non-Primary Color.” |
| Map: Contains substring |
If the Existing Text value is found anywhere inside the field’s string, then that field’s value is replaced entirely with the New Text. Example: If the field is Industry, with Existing Text "bank|lending", and New Text "Financial Services", then "Commercial Banking" becomes "Financial Services" |
Use when your text may appear inside a longer word or as a phrase, such as “bank” in “Commercial Banking,” “tutor” in “Tutoring,” or “higher ed” in “Higher Education.” The entire value is replaced. |
| Map: NOT contains substring |
Look for a specific value anywhere in the string of characters. If its not found, the entire field value is overwritten with the New Text. Example: If the field is Lead Source, with the Existing Text "Webinar|Trade Show|Conference" and New Text "Other," then "cold call" becomes "Other" |
Use to replace every value that lacks a string of characters with one value, such as changing any Lead Source that doesn’t contain "webinar" or "trade show" to "Other". |
| Map: Starts with |
Look for the specified value at the beginning of the data, and if found, the entire value is replaced with the specified text. Example: If the field is Industry, with Existing Text “Medical”, and New Text “Healthcare”, then “Medical Devices” becomes “Healthcare” |
Use to replace a value based on how it begins, such as lead sources that start with "Paid". |
| Map: Default value (unmapped) | Often used when adding several functions to one field. When the field value doesn't match any preceding functions, you can define a default value to apply. | Use as a catch-all when you’ve set up several functions for one field and want to assign a value to anything that matched none of them, such as “Other.” Because order matters, place it after your other functions. |
| Map: Numeric range |
Replace a number that falls within a range with a set value. Example: If the field is Employees, with Existing Text "100-200", and New Text "SMB", then "175" becomes "SMB" |
Use to replace numbers that fall within a range with a set value, such as changing employee counts between 100 and 200 to “SMB.” |
| Map: Regex |
Use a regular expression (regex) to find a pattern in your data and replace any occurrence with a specified value. Example: If the field is Postal Code, with Existing Text parameter "1[0-4]{1}[0-9]{3}", and New Text "New York", then "10013" becomes "New York" |
Use when the values follow a pattern that simple text can’t describe, such as a range of postal codes. |
| Map: US Zip Postal Code to US State (abbrev.) |
Use the zip/postal code value to write the two-character US state into a specified field. Example: If the field is Postal Code, with Target Field "State/Region", then "10013" becomes "NY" |
Use to fill a State/Region field from the postal code. |
Learn more about using Map functions.
Preserve Original Data by Copying to Custom Field
To preserve the original data, copy the Industry value to a custom field before you standardize it. A single operation can’t do both, so set up two templates and run them in sequence.
Template A: Copy the original value
- Field Name: Industry
- Function: Copy: Value
- Target Field: My Custom Field
Template B: Standardize the Industry value
- Field Name: Industry
- Function: Map: Contains word
- Existing Text: Camping|Recreation
- New Text: Outdoors
- Save each configuration as a template by clicking the Save button on the Template Menu.
- Add both templates to a Recipe in this order: Template A, then Template B. Template A needs to run first, because Template B overwrites the Industry value.
- Run the Recipe with a single click, or schedule it using automation so both templates run in sequence each time.
Using HubSpot Workflows or Salesforce Flow Automation to Update Industry
HubSpot and Salesforce users can run their industry update Recipes directly from HubSpot Workflows or Salesforce Flows. Learn more about integrating Insycle with HubSpot Workflows or Salesforce Flows.
Additional Resources
Related Help Articles
- Transform Data FAQs
- Standardize Industry Values before Importing Data
- Copy or Move Values Between Fields
- Module Overview: Transform Data
Related Blog Posts