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• What is Google Analytics data sampling and what’s so bad about it ?

  16 août 2019, par Joselyn Khor — Analytics Tips, Development
  What is Google Analytics data sampling, and what’s so bad about it ?

  Google (2019) explains what data sampling is :

  “In data analysis, sampling is the practice of analysing a subset of all data in order to uncover the meaningful information in the larger data set.”[1]

  This is basically saying instead of analysing all of the data, there’s a threshold on how much data is analysed and any data after that will be an assumption based on patterns.

  Google’s (2019) data sampling thresholds :

  Ad-hoc queries of your data are subject to the following general thresholds for sampling :
  [Google] Analytics Standard : 500k sessions at the property level for the date range you are using
  [Google] Analytics 360 : 100M sessions at the view level for the date range you are using (para. 3) [2]

  This threshold is limiting because your data in GA may become more inaccurate as the traffic to your website increases.

  Say you’re looking through all your traffic data from the last year and find you have 5 million page views. Only 500K of that 5 million is accurate ! The data for the remaining 4.5 million (90%) is an assumption based on the 500K sample size.

  This is a key weapon Google uses to sell to large businesses. In order to increase that threshold for more accurate reporting, upgrading to premium Google Analytics 360 for approximately US$150,000 per year seems to be the only choice.

  What’s so bad about data sampling ?

  It’s unfair to say sampled data is to be disregarded completely. There is a calculation ensuring it is representative and can allow you to get good enough insights. However, we don’t encourage it as we don’t just want “good enough” data. We want the actual facts.

  In a recent survey sent to Matomo customers, we found a large proportion of users switched from GA to Matomo due to the data sampling issue.

  The two reasons why data sampling isn’t preferable : 

  1. If the selected sample size is too small, you won’t get a good representative of all the data. 
  2. The bigger your website grows, the more inaccurate your reports will become.

  An example of why we don’t fully trust sampled data is, say you have an ecommerce store and see your GA revenue reports aren’t matching the actual sales data, due to data sampling. In GA you may be seeing revenue for the month as $1 million, instead of actual sales of $800K.

  The sampling here has caused an inaccuracy that could have negative financial implications. What you get in the GA report is an estimated dollar figure rather than the actual sales. Making decisions based on inaccurate data can be costly in this case. 

  Another disadvantage to sampled data is that you might be missing out on opportunities you would’ve noticed if you were given a view of the whole. E.g. not being able to see real patterns occurring due to the data already being predicted. 

  By not getting a chance to see things as they are and only being able to jump to the conclusions and assumptions made by GA is risky. The bigger your business grows, the less you can risk making business decisions based on assumptions that could be inaccurate. 

  If you feel you could be missing out on opportunities because your GA data is sampled data, get 100% accurately reported data. 

  The benefits of 100% accurate data

  Matomo doesn’t use data sampling on any of our products or plans. You get to see all of your data and not a sampled data set.

  Data quality is necessary for high impact decision-making. It’s hard to make strategic changes if you don’t have confidence that your data is reliable and accurate.

  Learn about how Matomo is a serious contender to Google Analytics 360. 

  Now you can import your Google Analytics data directly into your Matomo

  If you’re wanting to make the switch to Matomo but worried about losing all your historic Google Analytics data, you can now import this directly into your Matomo with the Google Analytics Importer tool.

  
  Take the challenge !

  Compare your Google Analytics data (sampled data) against your Matomo data, or if you don’t have Matomo data yet, sign up to our 30-day free trial and start tracking !

  References :

  [1 & 2] About data sampling. (2019). In Analytics Help About data sampling. Retrieved August 14, 2019, from https://support.google.com/analytics/answer/2637192

• Date and segment comparison feature

  31 octobre 2019, par Matomo Core Team — Analytics Tips, Development

  Get a clearer picture with the date and segment comparison feature

  What can you do with it ? What are the benefits ?

  Make informed decisions faster by easily comparing different segments and dates with each other.

  Compare report data for multiple segments next to each other

  

  Directly compare the behaviour of visitors from different segments e.g. customers with accounts vs. customers without accounts. Segment comparisons are a powerful way to compare different audience ; learn which ones perform better ; and in what way their actions differ. 

  Compare report data for two time periods next to each other

  

  See how your website performs compared to the previous month/week/year. Including seeing trends over those periods. Say, your business always picks up at the same times within a year, or there’s a sag in business for every user segment over this year and the last except one.

  By being able to compare date ranges you are able to get a quick overview of trends and period to period performance. Has a campaign worked better in September than in October ? Get an instant look by having the side-by-side comparison in Matomo.

  What is it capable of ?

  It lets you ask the question, “What is different ?”

  If you look at reports you’ll only see how people behave overall and if you look at specific segments you’ll see how they behave at face value, however, if you compare data together you’ll be quickly informed on what makes them unique. This data is still there when you don’t use the comparison feature, it’s just buried. Comparing data highlights discrepancies and leads to important questions and answers.

  For example, perhaps some class of users have very low engagement on a specific day compared to the rest of your visitors, and perhaps those users are responsible for an outsized proportion of churn. 

  Who could benefit from it, and why ?

  Everyone can benefit from using it (and probably should use it). It’s yours to experiment with ! You shouldn’t feel restricted to only comparing between the current and last period, or having questions before you start comparing. Follow your instincts and see what pops out when data from different segments is laid out next to each other.

  Where can you find it in Matomo ?

  • Segment comparison is activated by the new icon in the segment selector

  

  • Date comparison can be found in the calendar section of Matomo

  

  • The list of active comparisons is visible at the top of the page for all pages that support comparison
  • Comparisons are visible in every report that supports comparing data, and reports that do not support it will display a message saying so

  How do you use it ?

  • To compare segments, click the icon in the segment selector
  • To compare periods, click the ‘compare’ checkbox in the period selector, then select what period you want to compare it against in the dropdown (previous period, previous year, or a custom range)
  • When comparisons are active, view your reports as normal

  Take it away !

  The comparison feature is a new tool from Matomo 3.12.0 that highlights discrepancies and differences in data that can lead to more clarity and understanding, so we’d encourage everyone to use it. 

  Try it out today in your Matomo and see the power behind this new data comparison mode !

• Time-weighting frames of an animation using FFMPEG [duplicate]

  12 mars 2020, par Rohan S Byrne

  I’m using ’ffmpeg’ to make an mp4 out of hundreds of individual frames, which are timeslices from an experiment I’ve run. My problem is that the time sampling is not regular. I would like to somehow ’weight’ the frames according to their time index so that the framerate is fast when the sampling rate was fast and slow when the sampling rate was slow.

  I know how to do this sort of operation when taking, for instance, a time average using numpy arrays. I have no idea how to do it with ffmpeg.

  Any tips ?