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Log in to your LinkedIn account and navigate to the LinkedIn Campaign Manager. This is where you manage your ads and campaigns and can access the data you need to export.
In the Campaign Manager, select the campaign or campaigns you wish to export data from. Look for the reporting or analytics section, and choose the option to download or export the data. LinkedIn typically allows you to export data in CSV format, which is compatible with Google Sheets.
Once you have downloaded the CSV file, open it in a spreadsheet application like Microsoft Excel or any compatible software. Review the data to ensure that all required metrics and dimensions are present and that the data is structured correctly.
Go to Google Sheets (sheets.google.com) and open a new or existing spreadsheet where you want to import your LinkedIn Ads data. Make sure that you have the necessary permissions to edit the spreadsheet.
In Google Sheets, click on "File" in the menu, then select "Import." Choose the "Upload" tab, and then drag and drop your CSV file or select it from your computer. Follow the prompts to import the data, ensuring you choose the correct import options such as 'Replace data at selected cell' if needed.
Once imported, review the data in Google Sheets. You may need to format columns, adjust date formats, or clean up any discrepancies in the data. Utilize Google Sheets functionalities like data validation or conditional formatting to enhance data readability and accuracy.
While fully automated solutions require third-party tools, you can streamline future updates by developing a routine process. Set reminders to manually export and import data at regular intervals (e.g., weekly or monthly). Consider using Google Sheets functions like QUERY or FILTER to create dynamic reports that update based on the new data you import.
By following these steps, you can successfully move data from LinkedIn Ads to Google Sheets without relying on third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
LinkedIn ads helps businesses of any size achieve their goals and reach their target market. Over 850M active professionals are on LinkedIn. Target your audience them by job title, function, industry, and more.
LinkedIn Ads API provides access to a wide range of data related to LinkedIn advertising campaigns. The following are the categories of data that can be accessed through the API:
1. Ad Campaign Data: This includes data related to the performance of ad campaigns such as impressions, clicks, conversions, and spend.
2. Audience Data: This includes data related to the audience targeted in the ad campaigns such as demographics, job titles, industries, and locations.
3. Account Data: This includes data related to the LinkedIn advertising account such as account balance, billing information, and account settings.
4. Ad Creative Data: This includes data related to the ad creatives used in the campaigns such as ad formats, images, and headlines.
5. Conversion Tracking Data: This includes data related to the conversion tracking set up for the campaigns such as conversion events, conversion values, and conversion tracking tags.
6. Engagement Data: This includes data related to the engagement of the audience with the ad campaigns such as likes, comments, and shares.
7. Performance Data: This includes data related to the overall performance of the ad campaigns such as click-through rates, conversion rates, and cost per click.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
