Time is running out to shop your Universal Analytics statistics. Follow those steps to archive historical analytics earlier than the July 1 cut-off date.
Another Google Analytics four migration challenge cut-off date is rapid drawing near, and this cut-off date is hard set. On July 1, Google will delete all historical records from Universal Analytics properties. This deadline also influences Analytics 360 clients.
With little more than a month till the closing date, when you have now not executed so via now, your agency desires to prioritize archiving your historical data. There are three most important levels I advise for drawing near this task.
Phase 1: Make a plan
Before archiving records, it’s crucial to determine.
What specific statistics is critical to you?
Prioritize downloading information which you regularly discuss with, which include conversion and income statistics.
Make a complete list of the statistics you need to archive.
How a few years of statistics do you want to preserve?
Many of us had been the use of Google Analytics because the mid-2000s – does your employer need to archive records from nearly 20 years in the past?
Decide how some distance lower back you need to archive facts from. I advise, at minimum, to bear in mind archiving lower back to 2018 or to be able to make certain you have pre-pandemic records because the pandemic certainly presented statistics anomalies for lots businesses.
At what cadence do you evaluate records?
Consider how regularly you commonly document for your records. Is it weekly? Monthly?
Depending on the archiving method you pick in Phase 2, you could need to organize the information into precise time increments.
Phase 2: Choose an archiving technique
There are four principal alternatives to be had for archiving your Universal Analytics records. Each has its very own execs and cons, so pick a method primarily based on your group’s resources and capabilities.
Option 1: Manual file downloads
Pros: Easy for nearly all customers to do, loose
Cons: Time-ingesting, bulky, hard to get entry to facts for reporting later, limited to 5000 rows
While this is the very best method to recognize, it is also time consuming.
Following your plan for years, cadence and information points, you’ll want to go into each report within the Google Universal Analytics interface, set the date, dimension and metric settings as wanted.
Also, recall to trade the range of rows from the default of 10 to the maximum of 5,000 rows to make certain you capture as a lot records as viable.
Click the export button and export statistics to a Google Sheet, Excel or CSV. Repeat this procedure till you have downloaded all the records recognized to your archive plan.
Option 2: Download information to Google Sheets the usage of the Google Analytics add-on (fine alternative for tech beginners)
Pros: Fairly easy to enforce for most users with spreadsheet enjoy, loose, fast to download.
Cons: Restrictive to a fixed time-frame (e.G., monthly), each sheet has total information barriers, regularly encounters sampling issues.
This choice is reasonably simple for maximum customers to perform. Create a brand new Google Sheet and add the Google Analytics spreadsheetadd-on.
The add-on essentially makes use of the Google Analytics API to download records to Google Sheets but doesn’t require API programming knowledge to perform. Google has compiled a simple assessment of this technique on this assist record.
The first time you use the add-on, you’ll construct a document the usage of the add-on’s interface. But after the first document has been run, you can additionally truly replace the Report Configuration tab and create extra reviews directly in columns of that sheet.
You can also comfortably use formulas inside the Report Configuration sheet. Use the Dimensions and Metrics Explorer to locate the proper API code to go into into every discipline.
One downside of the Google Sheets method is that you could come across sampling if you pull an excessive amount of data right away (e.G., your complete 20-year dataset for classes) or your report is simply too specific (too many dimensions pulled collectively for a high degree of granularity).
When you run a record, you’ll see the sampling level at the file’s records tab in cellular B6. If your record includes sampled information, you can want to don’t forget reducing the quantity of facts in this particular pull, for example, you may break up the pull into two time frames.
However, if you just can’t keep away from sampling, check the facts sample percentage on the file. Then, at the Report Configuration tab, unhide rows 14-17 and the sampling size on row 15 to this level so that your records remains regular.
Tip: The add-on defaults to at least one,000 lines of facts in a file. Simply delete the 1,000 beneath the line classified “Limit” (typically row eleven).
Another disadvantage of the Google Sheets alternative is that every report is constrained to ten,000,000 cells. Typically, each sheet begins out with 26 columns (A to Z) and 1,000 default rows (or 26,000 cells).
If your downloaded records exceeds the ten,000,000 cell issue (which could very likely manifest), then you can want to have more than one Google Sheets to down load all of the records.
Option 3: Download facts using the Google Analytics API
Pros: Pulls statistics quickly once installation
Cons: Requires web development expertise and assets, doesn’t resolve the data sampling trouble, API quota boundaries
If you have got internet development sources that can paintings at the archiving challenge, they are able to pull the facts specified to your plan the usage of the Google Analytics API directly.
This works in addition to the aforementioned Google Sheets add-on alternative, but it’s a greater guide procedure in programming the API calls.
To find out about the way to use the API for this mission, visit Google’s archiving records web page and overview the second one bullet, which details numerous sources and issues for using the API for this facts export task.
Option four: Download data to BigQuery (nice alternative normal)
Pros: Simple to get entry to data later for reporting, accelerated data insights, most bendy for records
Cons: Complicated for beginners to set up initially, can contain prices for BiqQuery, may also require technical assets to installation, need to contain an extra tool
The predominant advantage of archiving your Universal Analytics facts to BigQuery is that BigQuery is a statistics warehouse that lets in you to invite questions of the information set via SQL queries to get your information right away. This is specifically beneficial in having access to this statistics for reporting later.
Analytics 360 customers
If you’re an Analytics 360 user, Google presents a local export to BigQuery. I recommend this approach. See instructions from Google.
Everyone else
If you’re no longer an Analytics 360 user, then you definitely’ll want to approach the BigQuery backup in another way due to the fact Google does not provide innate BigQuery backup alternatives in Universal Analytics for non-360 customers.
Here are the steps you’ll want to follow:
Step 1: Create a Google API Console challenge and allow BigQuery.
Log in to the Google APIs Console.
Create a Google APIs Console venture.
Navigate to the APIs table.
Activate BigQuery.
Step 2: Prepare your mission for BigQuery export.
Ensure Billing is enabled to your undertaking. You won’t want to pay anything, but it’ll vary depending on the usage and facts you’ve got.
If prompted, create a billing account.
Accept the free trial if it’s available.
Validate Billing enablement. Open your task at https://console.Cloud.Google.Com/bigquery, and try and create a records set inside the task. Click the blue arrow subsequent to the assignment name, then click Create records set. If you can create the data set, billing is setup efficiently. If there are any mistakes, make certain billing is enabled.
Add the service account to your assignment. Add analytics-processing-dev@device.Gserviceaccount.Com as a member of the assignment, and ensure that permission on the undertaking stage is about to Editor (instead of BigQuery Data Editor). The Editor role is required which will export information from Analytics to BigQuery.
If you are within the EU, please also evaluation additional requirements.
Step 3: Set up a unfastened trial of Supermetrics. Similar to the Google Sheets upload-on in choice 2 above, Supermetrics is a tool that facilitates non-technical users interface with and use APIs. They offer a free 14-day trial, which is in all likelihood all you’ll want for this mission since you’re simplest downloading the Universal Analytics information as soon as (not frequently).
Connect the BigQuery data source in the Supermetrics dashboard.
Step 4: In BigQuery, establish the relationship to Supermetrics.
Navigate to BigQuery, then to Data transfers.
Click + Create transfer.
Select your Google Analytics by way of Supermetrics as your supply and click on Enroll.
Fill within the switch information. See distinct commands on a way to set up a switch.
Under Third-birthday party connection, click on Connect supply.
Accept the settlement.
Click Authorize along with your Google information source.
Click Sign in with Google.
Sign in with the Google Account you operate with this records source. This doesn’t must be similar to the Google Account you use with Supermetrics.
Click Allow.
Select the money owed you’d like to encompass in your reporting and define the switch settings.
Click Submit.
Click Save.
Because you handiest want to switch the Universal Analytics statistics one time, you can also exchange the schedule at the transfer to On call for and then run the switch now.
Phase three: Ensure you’ve captured all of it
Before you do not forget the venture entire, make certain to double-take a look at your archived statistics to make sure you’ve captured everything you planned to archive.
On July 1, you may now not be capable of get right of entry to Universal Analytics statistics, either by using API or through the interface.
Opinions expressed in this text are those of the visitor creator and no longer necessarily Search Engine Land. Staff authors are listed here.