The time you invest in downloading this data is only as valuable as your use of it in the future. If you are having to group data due to row limitations or you’re just want your data broken up monthly or yearly – grouping this data into sheets will be extremely helpful!īelow we’ve grouped by Quarters to make future comparisons fast and easy. The data is very raw and basic so making sure you can actually find the data as well as understand it is probably the most important piece of this process… Grouping Data It might be extremely helpful to add notes/change labels to what makes the most sense for your team to each spreadsheet. Analyzing data in an excel spreadsheet is a different ballgame for some of us. Lets take a look at the data you accumulated. Remember, you will have to export each data set for each quarter.Įxporting will be the easiest portion of this process! Once you figure out your time frames and what information you want – this part just might be time consuming!īefore you do this repeatedly…. Organization is key in making this data useful in the future. Renaming and moving over datasets will make all of your Q4 data easy to find in one spreadsheet! Now, you can start using this spreadsheet for all of Q4. Once we click export to excel, this is what the data set looks like: See below.įrom this screen we can see we are exporting the amount of users from Octoto Dec 31, 2018. If you have some extra time, grabbing the whole year as an export is a good option as well to be able to use for a yearly comparison!įor example, if your app stopped collecting data in October 2019, start with collecting the last quarter of 2018. Our recommendation is to go back one year in quarterly chunks (if you’re not hitting the row limit before that). Your app might be 7 years old…going through 7 years could be a time consuming project. If that’s the case for you, don’t go farther than a year back! As long as you have something to compare 2020 with, moving forward all of your data should be in the same place. Let’s talk numbers and time frames… Google limits you to downloading 50,000 rows per export – so depending on the amount of data, users, and activity within your app will depend on how intricate this process gets.Īpps with a lot of traffic might have to run week by week exports on all data since you’ll probably be hitting the row limit.
Returning users (time frames will matter here) By going to behavior > behavior flow – this will give you an idea of the most prominent user paths in the time frame you selected! This will not be an exported excel doc, it will just be a PDF! This will be great to see quarter over quarter trends to make improvements in the UX moving forward.When and why was your app crashing? Remember to take advantage of the notes section when exporting for any reminders you think you might want in the future.
#Export timing app data download
If you don’t export anything else, export this. When were users using your app most often? How many users did the last release or announcement bring in? Maybe you’re doing a similar announcement you did 3 years ago and you want to be able to see the traffic generated.Google Analytics has so much information, being able to drill down what you want out of the data might be the biggest decision in the entire process. In this process of exporting your data, the most important piece of the puzzle is figuring out WHAT information you need and/or want. Come December 2020 and you’re analyzing your data, knowing how your app did in previous years is the only way you will be able to measure your successes and places for improvement within your app. Data is only going to be valuable when you are able to compare it.
Even if you don’t think you need the data moving forward, it is worth spending a little bit of time capturing big picture analytics just in case you ever need it in the future.
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