What is Data Import?

Wondering what data import is in Google Analytics? How you can use it, with explanations for the data types? Good on you, we think it is awesome.

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Data Import: What it is and how can you use it?

Data Import is underused, again in our humble opinion. If the previous lesson was to get you to think about creating new dimensions and metrics, Data Import lets you upload data from external sources and combine it with data you collect. Naturally, we have a handy explainer on data import in Google Analytics. This post will explore the concept and give you some ideas for the use cases. 

First.. You need a ‘key’ to ‘lock’ the data together.  You don’t have to create Custom Dimensions and Metrics in order to use Data Import. If it already exists, you just need to work out what data you are going to Import. Data is usually uploaded into your GA account with a formatted CSV file. You can also look at pushing data into GA using the Analytics Management API, but this will take some additional help from your dev team. 

A really good use case for data import, and to really highlight the use-cases for this feature, is cost data for marketing programs that are not part of the Google party.  If you have linked your Google Ads account to your Google Analytics property, it will push in additional data into your acquisition reports, things like the cost of that traffic, the Impressions, and the click-through rate.  But what if you use in Bing or another search engine that offers PPC as a channel? 

Well, you could create a dataset and import that data into Google Analytics which means that when you’re looking at your data you see a better apple for apple comparison.  

Another example, we worked with a business that specialized in outdoor activities (for those of you that take pleasure in the great outdoors). A causal factor that really impacted sales was the weather.  So, we imported the weather reports. When we did our analysis for their marketing campaigns which were sent to the board. It helped to show why, at times, marketing campaigns were really good but if the weather was really bad sales went down. 

Now since the arrival of Google Data Studio, you may not need to import data into Google Analytics.  You may want to look into something called ‘data blending’. This is where Google Data Studio will link up with another data source. This could be a Google Sheet or an Excel document and you can blend that data together.  This would be the go-to option if you knew a causal factor, or it wasn’t necessary for you to import it directly into Google Analytics to get the information you needed.

So, it’s really thinking about information that sits in different data pots that would be really useful for you to see it together in Google Analytics to help you understand causality,  and to get a better insight into how your marketing campaigns are working, your content, and how well this is being received by your visitors.

Data Types for Data Import 

When you are looking at Data Import, there are three types of data that you can import. 

1- Hit-Data Import 

Lets you send hit data directly into Analytics. This is an alternative way to get data into your account outside of using the tracking code, or Measurement Protocol. You would use this for Refund Data. If you want to import your internal E-Commerce reporting with GA so you can see Refund Data, this is an option for you.  

2- Extended-data Import  

This lets you upload data that has already been collected, processed or being processed for your reporting views. You may need to create a Custom Dimension or Metric for this to happen. 

  • User Data—No Personal Identifiable information here, but if you have user metadata, from your CRM or equivalent data pot you can load this in. Think about things like loyalty rating, lifetime customer value, Monthly Recurring Revenue, Churn Rate. 
  • Campaign Data—can be used to expand and reuse your existing non-Google campaign codes by importing ad campaign-related dimensions, such as source, new campaign classifications, or variations of the campaign.
  • Geographical Data—create custom geographical regions, that are better aligned with your business’ organization. Think about businesses that want to move out from GA’s settings for say, the USA as a whole. You may wish to put in your own Sales Regions to help analysis 
  • Content Data—who wrote the content, when was it published, what type of article, did it have a video, how many words, etc. 
  • Product Data—this uses the SKU as a key and you need to have Enhanced E-Commerce for this to work. Use it to gain better-merchandising insights by importing product metadata, such as size, color, style, or other product-related dimensions.
  • Custom Data—basically anything that doesn’t fit in the above 🙂 

3- Summary- Data Import 

This lets you import metrics into the reporting views that have already processed your data. You use this option for Cost Data. So anything that you pay for outside of the Google Ecosystem. 

Think about the non-Google costs for marketing campaigns, ad network clicks, cost, and impression data to gain a more complete picture of your ad spend. Twitter Ads, Bing Ads, Facebook Ads, can all be pushed here. 

You can create up to 100 data sets. That doesn’t mean you can only load up data 100 times. It just means you can create 100 data sets to load. 

So, if we were to create data sets for our non-Google marketing I would create: 

1- One set for Twitter Ads

2- One set for Bing Ads 

For your GA audit, have a look at your Property> Data Import to see if you have anything in there already. If you see a use for this feature, write it in your measurement plan. 
Did this content tickle your fancy? Well, we have something we think your brain will love. Our online Google Analytics course is packed with everything you need to understand how GA works. So, if you have been using it for a while, but feel like you are not making the most out of its potential. Well, walk this way.

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What is Data Import?

Wondering what data import is in Google Analytics? How you can use it, with explanations for the data types? Good on you, we think it is awesome.

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