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title: How to structure your marketing data for analytics
description: Perfect your marketing strategy with these precise tagging practices.
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# How to structure your marketing data for analytics

 October 09, 2024

[Home](https://24313776.hs-sites.com/)>[Blog](https://24313776.hs-sites.com/blog)>How to structure your marketing data for analytics

# How to structure your marketing data for analytics

 October 09, 2024

**Your marketing campaigns can capture a wealth of data** that lays the foundation for better decision-making—but it requires proactive setup on your part. Implementing new, consistent structures to your data today can help with descriptive, predictive, prescriptive, and AI-powered analytics in the future. In fact, many of these analyses require about two years of quality data to be successful, so the best time to start is now.

We’ve identified the key steps to implement today that will ensure your data can drive actionable insights in the future. Of course, you’ll proceed with setting campaign goals as usual. Understanding your objectives and clearly identifying how you want the campaign to perform (impressions, clicks, conversions, etc.) and determining your target audience remain foundational. Establishing marketing channels up front (e.g., email, paid search, display ads, social) is essential as this will directly impact tagging practices as we discuss below.

‍

##### **Follow these best practices for setting up media tags.**

Once your campaign is established and you know your channels, you can determine which tools and platforms you’ll gather data on and need to tag. Because multiple departments or people may handle different channels, you should create a standard, documented naming convention from the outset that everyone is trained to use.  

This is incredibly important because data from each source will need to be brought together in an analytics tool in the future. For a tool to systematically identify and group campaign data together, tag parameters from the various sources must be identical, including the same spelling, capitalization, and special characters.  

Other best practices include:

- Always tag media in a way that helps **identify each medium** you're using. For example: email, social, or display ads.
- Give a **timing description **in the tag. For example: Q4_2024.
- Avoid using any **special characters **in your tags, as they can potentially cause issues with URL encoding. **‍**
- **Reference or use online tools or parameters** for successful tagging. For example, Google has the [UTM builder](https://ga-dev-tools.google/campaign-url-builder/), a tool that allows you to plug in a URL and build out a UTM. (You can also create your own UTM builder in-house to force certain naming conventions, which can be useful for large teams.) **‍**
- **Create a standards document** on how to build and structure tags. If you don’t, you risk each team doing it differently, undermining your ability to effectively use AI in the future because you’ll lack that through-line. Creating and agreeing on the standards document is a great reference for current and new team members. It should include things like date structure, case preferences such as sentence or all lower, channel names (social vs. insta vs. Instagram), etc.
- Remember to **audit regularly**. Once you've started collecting data, you'll want to review for anomalies. Be sure to review for and remove duplications. This prepares the data for joining with other data sources later on.

‍

##### **How doing this now impacts your analysis later.**

Starting now gives you an advantage that grows over time, beacuse you cannot go back and restructure and retag past data. Having a consistent tagging strategy is key for descriptive analytics, such as tracking campaign ROI and understanding past user behaviors. A greater understanding of your last campaign is a benefit that can be applied to decisions for your next campaign.

If your team is looking to deploy predictive, prescriptive, or AI analysis, the most important thing to note is that **you need two years of tagged, structured campaign data**. With only one year, seasonal changes will look like anomalies and can mislead outputs from these advanced analyses.

The takeaway? The more data, the better. So, start developing and sharing guidelines with all departments that handle the various channels now and build a viable foundation for analysis that allows you to confidently make data-driven decisions.

‍

##### **Need help structuring your marketing data for analytics?**

If your team needs help tagging marketing data for analytics, [OneMagnify](https://www.onemagnify.com/?__hstc=17958374.41d3c5385ec1fc590ac1297b72d3cd83.1704734178587.1718400503992.1718630575644.94&__hssc=17958374.3.1718630575644&__hsfp=250426774) is here to help. Reach out today and our experts can work with you to develop and implement strategies based on your organization's goals.

##### Written by Admin

- [Using Salesforce for B2B loyalty programs: Overcoming legacy platforms and technical gaps](https://24313776.hs-sites.com/blog/using-salesforce-for-b2b-loyalty-programs-overcoming-legacy-platforms-and-technical-gaps)
- [Analytics 101: Four types of analytics](https://24313776.hs-sites.com/blog/analytics-101-four-types-of-analytics)

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