Time is jogging out to keep your Universal Analytics facts. Follow those steps to archive ancient analytics earlier than the July 1 cut-off date.
Another Google Analytics four migration mission deadline is fast approaching, and this closing date is tough set. On July 1, Google will delete all ancient information from Universal Analytics houses. This closing date additionally affects Analytics 360 customers.
With little more than a month till the cut-off date, when you have not finished so through now, your business enterprise wishes to prioritize archiving your ancient statistics. There are 3 fundamental stages I recommend for drawing close this challenge.
Phase 1: Make a plan
Before archiving data, it’s crucial to decide:
What unique information is important to you?
Prioritize downloading data that you frequently discuss with, along with conversion and sales facts.
Make a complete listing of the information you need to archive.
How a few years of statistics do you need to preserve?
Many people were using Google Analytics for the reason that mid-2000s – does your organization need to archive statistics from almost 20 years ago?
Decide how far returned you want to archive information from. I suggest, at minimal, to keep in mind archiving again to 2018 or in an effort to ensure you have got pre-pandemic information because the pandemic sincerely offered facts anomalies for many groups.
At what cadence do you review facts?
Consider how frequently you usually report on your information. Is it weekly? Monthly?
Depending on the archiving approach you choose in Phase 2, you may want to prepare the statistics into specific time increments.
Phase 2: Choose an archiving approach
There are four primary options to be had for archiving your Universal Analytics facts. Each has its own pros and cons, so pick out a method primarily based on your crew’s assets and competencies.
Option 1: Manual file downloads
Pros: Easy for nearly all customers to do, loose
Cons: Time-ingesting, cumbersome, difficult to get right of entry to facts for reporting later, confined to 5000 rows
While this is the perfect technique to understand, it is also time ingesting.
Following your plan for years, cadence and data points, you’ll need to go into each report within the Google Universal Analytics interface, set the date, measurement and metric settings as wanted.
Also, don’t forget to exchange the quantity of rows from the default of 10 to the most of 5,000 rows to make certain you capture as a good deal facts as viable.
Click the export button and export statistics to a Google Sheet, Excel or CSV. Repeat this manner till you have downloaded all of the records recognized on your archive plan.
Option 2: Download facts to Google Sheets the usage of the Google Analytics add-on (exceptional alternative for tech beginners)
Pros: Fairly easy to put in force for maximum customers with spreadsheet enjoy, free, speedy to download.
Cons: Restrictive to a hard and fast timeframe (e.G., month-to-month), every sheet has overall facts limitations, frequently encounters sampling issues.
This option in all fairness simple for most customers to perform. Create a brand new Google Sheet and upload the Google Analytics spreadsheetadd-on.
The add-on basically uses the Google Analytics API to down load information to Google Sheets but doesn’t require API programming information to function. Google has compiled a basic overview of this technique on this help file.
The first time you operate the upload-on, you’ll construct a file the usage of the add-on’s interface. But after the primary report has been run, you can also certainly replace the Report Configuration tab and create additional reviews immediately in columns of that sheet.
You can also simply use formulas in the Report Configuration sheet. Use the Dimensions and Metrics Explorer to discover the right API code to enter into each area.
One drawback of the Google Sheets method is that you may come upon sampling in case you pull an excessive amount of information immediately (e.G., your entire 20-year dataset for periods) or your document is simply too specified (too many dimensions pulled collectively for a excessive level of granularity).
When you run a file, you’ll see the sampling level at the report’s statistics tab in mobile B6. If your record contains sampled facts, you may want to consider decreasing the quantity of facts on this specific pull, as an example, you might break up the pull into two time frames.
However, if you just can’t avoid sampling, test the data pattern percent on the file. Then, at the Report Configuration tab, unhide rows 14-17 and the sampling size on row 15 to this degree in order that your records remains steady.
Tip: The upload-on defaults to 1,000 strains of information in a record. Simply delete the 1,000 beneath the road labeled “Limit” (generally row 11).
Another drawback of the Google Sheets option is that every report is limited to 10,000,000 cells. Typically, every sheet starts out with 26 columns (A to Z) and 1,000 default rows (or 26,000 cells).
If your downloaded information exceeds the 10,000,000 mobile hindrance (that could very probably appear), then you may want to have a couple of Google Sheets to download all the facts.
Option three: Download records the usage of the Google Analytics API
Pros: Pulls facts quickly as soon as installation
Cons: Requires internet improvement understanding and assets, doesn’t clear up the data sampling issue, API quota limitations
If you have web improvement sources which could paintings at the archiving challenge, they could pull the facts exact in your plan the use of the Google Analytics API immediately.
This works further to the aforementioned Google Sheets add-on alternative, however it’s a greater manual process in programming the API calls.
To find out about a way to use the API for this task, visit Google’s archiving data page and assessment the second one bullet, which information numerous resources and issues for using the API for this statistics export undertaking.
Option 4: Download information to BigQuery (satisfactory option standard)
Pros: Simple to get admission to facts later for reporting, improved data insights, most bendy for information
Cons: Complicated for beginners to installation initially, can involve costs for BiqQuery, may additionally require technical sources to set up, need to involve a further device
The principal gain of archiving your Universal Analytics statistics to BigQuery is that BigQuery is a information warehouse that allows you to ask questions of the records set via SQL queries to get your records very quickly. This is specially beneficial in getting access to this statistics for reporting later.
Analytics 360 customers
If you are an Analytics 360 person, Google provides a local export to BigQuery. I recommend this method. See commands from Google.
Everyone else
If you’re no longer an Analytics 360 consumer, you then’ll need to method the BigQuery backup otherwise due to the fact Google does no longer provide innate BigQuery backup options in Universal Analytics for non-360 customers.
Here are the stairs you’ll need to observe:
Step 1: Create a Google API Console project and permit BigQuery.
Log in to the Google APIs Console.
Create a Google APIs Console assignment.
Navigate to the APIs desk.
Activate BigQuery.
Step 2: Prepare your mission for BigQuery export.
Ensure Billing is enabled on your challenge. You may not want to pay anything, but it will range relying on the usage and data you have got.
If triggered, create a billing account.
Accept the free trial if it’s available.
Validate Billing enablement. Open your mission at https://console.Cloud.Google.Com/bigquery, and try to create a facts set in the undertaking. Click the blue arrow subsequent to the challenge name, then click Create facts set. If you may create the data set, billing is setup efficaciously. If there are any errors, make certain billing is enabled.
Add the provider account to your undertaking. Add analytics-processing-dev@machine.Gserviceaccount.Com as a member of the venture, and make certain that permission at the mission stage is about to Editor (rather than BigQuery Data Editor). The Editor function is required with the intention to export facts from Analytics to BigQuery.
If you are inside the EU, please additionally evaluate additional necessities.
Step 3: Set up a unfastened trial of Supermetrics. Similar to the Google Sheets upload-on in option 2 above, Supermetrics is a tool that enables non-technical customers interface with and use APIs. They provide a loose 14-day trial, that is in all likelihood all you’ll need for this assignment because you’re best downloading the Universal Analytics facts as soon as (no longer regularly).
Connect the BigQuery data supply in the Supermetrics dashboard.
Step 4: In BigQuery, set up the relationship to Supermetrics.
Navigate to BigQuery, then to Data transfers.
Click + Create transfer.
Select your Google Analytics through Supermetrics as your source and click on Enroll.
Fill in the switch information. See specific commands on a way to installation a switch.
Under Third-celebration connection, click Connect supply.
Accept the agreement.
Click Authorize together with your Google statistics source.
Click Sign in with Google.
Sign in with the Google Account you operate with this statistics supply. This doesn’t need to be the same as the Google Account you use with Supermetrics.
Click Allow.
Select the money owed you’d like to consist of on your reporting and define the switch settings.
Click Submit.
Click Save.
Because you only need to switch the Universal Analytics statistics one time, you could also change the time table at the transfer to On demand and then run the switch now.
Phase 3: Ensure you’ve captured all of it
Before you don’t forget the venture complete, make sure to double-take a look at your archived information to make sure you’ve captured the whole lot you deliberate to archive.
On July 1, you may now not be capable of access Universal Analytics facts, both by means of API or thru the interface.
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