One other Google Analytics 4 migration mission deadline is quick approaching, and this deadline is difficult set. On July 1, Google will delete all historic knowledge from Common Analytics properties. This deadline additionally impacts Analytics 360 prospects.
With little greater than a month till the deadline, you probably have not achieved so by now, your group must prioritize archiving your historic knowledge. There are three most important phases I like to recommend for approaching this mission.
Section 1: Make a plan
Earlier than archiving knowledge, it’s vital to resolve:
What particular knowledge is vital to you?
- Prioritize downloading knowledge that you just frequently check with, reminiscent of conversion and gross sales knowledge.
- Make a full checklist of the information you’ll want to archive.
What number of years of knowledge do you wish to hold?
- Many people have been utilizing Google Analytics for the reason that mid-2000s – does your group have to archive knowledge from practically 20 years in the past?
- Determine how far again you wish to archive knowledge from. I like to recommend, at minimal, to think about archiving again to 2018 or so to make sure you have pre-pandemic knowledge for the reason that pandemic actually offered knowledge anomalies for a lot of corporations.
At what cadence do you evaluation knowledge?
- Contemplate how typically you sometimes report in your knowledge. Is it weekly? Month-to-month?
- Relying on the archiving methodology you select in Section 2, you could want to prepare the information into particular time increments.
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Section 2: Select an archiving methodology
There are 4 most important choices out there for archiving your Common Analytics knowledge. Every has its personal execs and cons, so select a way based mostly in your workforce’s sources and expertise.
Choice 1: Handbook file downloads
- Execs: Simple for nearly all customers to do, free
- Cons: Time-consuming, cumbersome, troublesome to entry knowledge for reporting later, restricted to 5000 rows
Whereas that is the simplest course of to grasp, it’s also time consuming.
Following your plan for years, cadence and knowledge factors, you’ll want to enter every report within the Google Common Analytics interface, set the date, dimension and metric settings as wanted.
Additionally, keep in mind to alter the variety of rows from the default of 10 to the utmost of 5,000 rows to make sure you seize as a lot knowledge as potential.
Click on the export button and export knowledge to a Google Sheet, Excel or CSV. Repeat this course of till you could have downloaded all the knowledge recognized in your archive plan.
Choice 2: Obtain knowledge to Google Sheets utilizing the Google Analytics add-on (best choice for tech novices)
- Execs: Pretty easy to implement for many customers with spreadsheet expertise, free, quick to obtain.
- Cons: Restrictive to a set timeframe (e.g., month-to-month), every sheet has whole knowledge limitations, typically encounters sampling points.
This selection is pretty easy for many customers to carry out. Create a brand new Google Sheet and add the Google Analytics spreadsheetadd-on.
The add-on basically makes use of the Google Analytics API to obtain knowledge to Google Sheets however doesn’t require API programming data to function. Google has compiled a primary overview of this method on this assist doc.
The primary time you utilize the add-on, you’ll construct a report utilizing the add-on’s interface. However after the primary report has been run, it’s also possible to merely replace the Report Configuration tab and create further experiences straight in columns of that sheet.
You can even conveniently use formulation within the Report Configuration sheet. Use the Dimensions and Metrics Explorer to search out the correct API code to enter into every discipline.
One disadvantage of the Google Sheets methodology is that you could be encounter sampling if you happen to pull an excessive amount of knowledge without delay (e.g., your complete 20-year dataset for classes) or your report is just too detailed (too many dimensions pulled collectively for a excessive degree of granularity).
Whenever you run a report, you’ll see the sampling degree on the report’s knowledge tab in cell B6. In case your report incorporates sampled knowledge, you could wish to think about decreasing the quantity of knowledge on this specific pull, for instance, you would possibly break up the pull into two time frames.
Nevertheless, if you happen to simply can’t keep away from sampling, test the information pattern share on the report. Then, on the Report Configuration tab, unhide rows 14-17 and the sampling dimension on row 15 to this degree in order that your knowledge stays constant.
Tip: The add-on defaults to 1,000 traces of knowledge in a report. Merely delete the 1,000 beneath the road labeled “Restrict” (sometimes row 11).
One other disadvantage of the Google Sheets possibility is that every file is restricted to 10,000,000 cells. Usually, every sheet begins out with 26 columns (A to Z) and 1,000 default rows (or 26,000 cells).
In case your downloaded knowledge exceeds the ten,000,000 cell limitation (which might very probably occur), then you could have to have a number of Google Sheets to obtain all the knowledge.
Choice 3: Obtain knowledge utilizing the Google Analytics API
- Execs: Pulls knowledge rapidly as soon as arrange
- Cons: Requires net improvement data and sources, doesn’t clear up the information sampling problem, API quota limitations
If in case you have net improvement sources that may work on the archiving mission, they will pull the information detailed in your plan utilizing the Google Analytics API straight.
This works equally to the aforementioned Google Sheets add-on possibility, nevertheless it’s a extra handbook course of in programming the API calls.
To study find out how to use the API for this mission, go to Google’s archiving data web page and evaluation the second bullet, which particulars a number of sources and concerns for utilizing the API for this knowledge export mission.
Choice 4: Obtain knowledge to BigQuery (best choice general)
- Execs: Easy to entry knowledge later for reporting, elevated knowledge insights, most versatile for knowledge
- Cons: Sophisticated for novices to arrange initially, can contain charges for BiqQuery, could require technical sources to arrange, have to contain a further software
The principle advantage of archiving your Common Analytics knowledge to BigQuery is that BigQuery is an information warehouse that lets you ask questions of the information set by means of SQL queries to get your knowledge in a short time. That is particularly helpful in accessing this knowledge for reporting later.
Analytics 360 customers
If you’re an Analytics 360 consumer, Google offers a local export to BigQuery. I like to recommend this methodology. See directions from Google.
Everybody else
In case you’re not an Analytics 360 consumer, then you definately’ll have to method the BigQuery backup in another way as a result of Google doesn’t present innate BigQuery backup choices in Common Analytics for non-360 customers.
Listed here are the steps you’ll wish to observe:
- Step 1: Create a Google API Console mission and allow BigQuery.
- Log in to the Google APIs Console.
- Create a Google APIs Console mission.
- Navigate to the APIs desk.
- Activate BigQuery.
- Step 2: Put together your mission for BigQuery export.
- Guarantee Billing is enabled on your mission. You might not have to pay something, however it’ll differ relying on the utilization and knowledge you could have.
- If prompted, create a billing account.
- Settle for the free trial if it’s out there.
- Validate Billing enablement. Open your mission at https://console.cloud.google.com/bigquery, and attempt to create an information set within the mission. Click on the blue arrow subsequent to the mission title, then click on Create knowledge set. In case you can create the information set, billing is setup appropriately. If there are any errors, make sure that billing is enabled.
- Add the service account to your mission. Add [email protected] as a member of the mission, and make sure that permission on the mission degree is ready to Editor (versus BigQuery Information Editor). The Editor position is required in an effort to export knowledge from Analytics to BigQuery.
- If you’re within the EU, please additionally evaluation further necessities.
- Step 3: Arrange a free trial of Supermetrics. Much like the Google Sheets add-on in possibility 2 above, Supermetrics is a software that helps non-technical customers interface with and use APIs. They provide a free 14-day trial, which is probably going all you’ll want for this mission because you’re solely downloading the Common Analytics knowledge as soon as (not frequently).
- Join the BigQuery knowledge supply within the Supermetrics dashboard.
- Step 4: In BigQuery, set up the connection to Supermetrics.
- Navigate to BigQuery, then to Information transfers.
- Click on + Create switch.
- Choose your Google Analytics by Supermetrics as your supply and click on Enroll.
- Fill within the switch particulars. See detailed directions on find out how to arrange a switch.
- Underneath Third-party connection, click on Join supply.
- Settle for the settlement.
- Click on Authorize together with your Google knowledge supply.
- Click on Register with Google.
- Register with the Google Account you utilize with this knowledge supply. This doesn’t should be the identical because the Google Account you utilize with Supermetrics.
- Click on Enable.
- Choose the accounts you’d like to incorporate in your reporting and outline the switch settings.
- Click on Submit.
- Click on Save.
Since you solely have to switch the Common Analytics knowledge one time, it’s also possible to change the schedule on the switch to On demand after which run the switch now.
Section 3: Make sure you’ve captured all of it
Earlier than you think about the mission full, remember to double-check your archived knowledge to make sure you’ve captured every little thing you deliberate to archive.
On July 1, you’ll now not be capable of entry Common Analytics knowledge, both by API or by means of the interface.
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