Our main challenge in Google Analytics at the time is to measure the success of our magazine articles.
The problem is that views grow over time so in any timeframe we always have the older articles overshadowing the newer ones. Sidenote: The same problem occurs for measuring social media post success.
My idea of a solution is to measure the rate by which views on articles grow. An article that has a higher growth of views is much more successful than an older article with more views, but with a lower growth rate.
Alternatively something like "views within the first week(s) of publishing this individual article" would also be a good metric.
Unfortunately to some extent also the growth rates rely on this publishing period of individual articles if we are interested in an eternal high score of articles. But since we are mainly interested in recent articles, growth rate would still give us the desired result of showing the most successful recent articles.
Has anyone dealt with the same challenges and found any solution to this, in best case with Google Analytics?
These examples may help, of which I have direct experience.
In the data layer we included a date of publication for the article and then used this to determine growth. This was taken from the CRM and was relatively straightforwards for the dev team. This was stored as a custom dimension in Google Analytics.
We had nothing in the data layer but instead a I just used the date on which page views started appearing as a proxy for date of publication. Not entirely reliable, and you may want to filter by views >5, or whatever is appropriate, to avoid any hits from editors or staff before a page is visible in the site navigation.
In both cases I was exporting data either to Google Sheets (using for example the Google Analytics API addon for sheets) or BigQuery, where it was relatively straight forwards to identify the first date and then calculate, for example, views per day. In your case it would be having a function which looks at the date of publication + 7 days. You may also be able to achieve this with Google Data Studio or similar dashboarding platform.
Related
This seems really basic but i am struggling with it
We have a client who runs a travel website.
They have a few different search bars eg Flights, Hotels, Carhire.
I am trying to track the performance of each... "What % of people completed a sale that ran a Flight search." Same for Hotel, and for Car hire
Any ideas for the best way to get this info in GA?
Many thanks
There are a few ways to get this information, each with their pros and cons. The options that I see immediately available are segments and goals.
Segments are great because they are retrospective and generally more flexible, with the ability to be changed if you find your criteria isn't quite right. You create here, and specify sessions that go through search results pages etc:
Then you can create another segment for booking confirmation page, and any other intermediary steps that you'd like to report on. The main con of segments is that you can only pull in 4 at a time, but if you have more you can pull them 4 at a time and copy+paste the data into an excel sheet or google sheet. Segments can also be pulled via the Core Reporting Api and DataStudio which makes them great for automating into dashboards.
Goals are cool because they pull into the default reports, and basically track sessions through a particular page, event or sequence. The main con I see and the reason is that I don't use them is that they only start tracking fro mthe time you create them , and if you change the configuration it does not impact historical data, so your data can get messed up quickly if you don't have sandbox GA views or sandbox goals for your testing before putting it into a dedicated goal slot. You can also only have 10 or 20 goals depending on your plan, so once data is tracked against that goal you can't remove or clear it.
I run a monthly report which tracks session views by region, most popular knowledge articles, deflection rates, most popular product pages, software download stats, etc.
We have a new ELT member who is keen to get into the numbers around our contact centre. As I only look after the support site I need only concern myself with putting together a report which outlines what I feel will be useful information around web traffic. I want the report to be brief, and to highlight 4-5 key metrics.
Please can I have some suggestions for data you think would be useful given the target audience?
So far I am considering:
Deflection rates
Bounce rates.
Time on page
Most popular software downloads.
Global session views year to date.
Any help would be really appreciated. Thanks!
I think those metrics are great. Ideally, the value in the data comes from slicing your metrics with a dimension, ie pivoting. For example, bounce rate as an average means little whereas bounce rate by Content Group or Device Category would be more interesting.
Speaking of Device Category, consider completely isolating the metrics for Mobile vs Desktop+Tablet. Those experiences are so drastically different you'd be doing a disservice to average those metrics together.
Lastly, I'd say this new ETL member should get their own access to GA and learn how to pull the data need. GA now offers machine learning insights that quickly surface relevant drivers in metrics; a static approach to KPI reporting is becoming increasingly obsolete.
We are using the free level of GA and have been creating reports using Custom Dimensions and Metrics since last summer.
We also use the Google Sheets Analytics add-on to post process data pulled from the API.
Overnight on 16-17 May (UK Time), our reports suddenly started showing as being sampled. Prior to that we had no sampling at all, as our reports are scheduled so I can look back through the revision history to see changes made when the scheduled reports run.
This sampling is occurring in custom reports viewed in the GA platform and in GA sheets. I've done some analysis and it appears to only occur at the point that more than one Custom Dimension is added to a report, or when the GA dimensions ga:hour or ga:dateHour are used (ga:date does not trigger sampling).
All our Custom Dimensions and Custom Metrics are set at Hit level (I've read a post where it was claimed to be due to mixing scopes on Dimensions & Metrics, but we are not doing this).
If I reduce the date range of a query (suggested as a solution on many blogs), the sampling level actually gets worse rather than better.
For the month of May we didn't even hit 4k sessions at property level. I can't find any reference anywhere to any changes being made to GA that would cause sampling to apply to our reports (change documentation, Google Blogs etc).
Is anyone else experiencing this or can anyone shed any light on why this might be happening? Given how we use GA if we can't resolve this then it's a year of work down the drain, so I'm really keen to at least know why this has suddenly happened even if ultimately nothing can be done about it.
I'm looking to use google analytics for its web interface only. A large dataset such as gasoline prices would be submitted to analytics via the api and viewed. Is this possible? Or is analytics purely tailored to viewing website statistics?
The Google Analytics data model is really geared toward datasets that can be thought of in terms of users, sessions, and hits (hits being things like pageviews and events).
If your data can be thought of in these terms, it will probably work. If, on the other hand, you're trying to do things like joins or calculate averages or other statistical operations, you're probably better of using something else.
While the others are correct, Google Analytics is geared towards users, sessions, and hits. It is none the less simply an application for data analysis. The question will be how to get the data into the system.
I think you need to give us a little more information about your data set. But let me assume a few things.
You have a dataset with gasoline prices over a period of days.
you have a dataset with gasoline prices for different gas stations.
It would be really nice if this wasn't old data that this is new gas prices coming in.
If I had this dataset I could insert it into Google Analytics. Directly using the measurement protocol.
The measurement protocol has a few required things, the first being hit type. 'pageview', 'screenview', 'event', 'transaction', 'item', 'social', 'exception', 'timing'. the second would cid or session id.
Now cid I think I would probably set to the different gas stations and probably add a custom dimension with the gas station name.
As for hit I think I would probably say screenview and make an application Google Analytics account. Mainly because well this isn't a website its a little different.
Then every time the price of Gas changes I would send a screenview, cid of the station with the custom dimension of the station, add a custom metric with the price.
The main problem you are going to have is that Google analytics doesn't handle old data well. If you are going to insert this data with a date associated the date and time cant be grater then 4 hours ago or the server wont process it.
Have you considered putting it in big Query instead?
This question really is to broad or opinion based, but it was fun to consider.
It is possible to send all kinds of hits with the Measurement Protocol. But Philip is correct in stating that the data model is largely geared towards users, sessions and hits. But you could probably get a good ways with custom dimensions and metrics.
I'm currently researching a solution to monitor the performance of specific sections of a page. For example, you have a simple page with 2 images with links to other pages. You are driving lots of traffic to this page and you are experimenting with different contents on that page.
6 months after, you want to see which section of the page performed better with what kind of specific imges.
Let's imagine you require a report that should tell you the following: on average, the first spot performs better, but last week the image was bad and that's why you had less conversion from that spot.
I'd like to use such a system on a high-traffic homepage of an eCommerce website, in order to better monitor the usage of the selling spots.
I was thinking to use Google Analytics events with a positioning scheme (splitting the website in columns and rows, giving to each cell an identification ID such as a1 for column a, row 1) and keeping a local datawarehouse of creatives (images, promotions etc.), but apparently, after 10.000.000 hits per month, Analytics is recommending the premium version which is quite pricey (12k USD per month, 1 year upfront payment).
I was thinking about PIWIK as an alternative, but there is no event tracking there - or am I missing anything?
Looking forward to hearing your input on this matter.
You're better off with a provider like Optimizely for this use case. Still gonna be expensive, but it'll more quickly get you the information you need to make decisions.
We normally use multi variation tests or A/B tests to measure the success of user interfaces. Google Analytics have this feature and it is free.
This links maybe useful
https://www.youtube.com/watch?v=yDWTMOC_Dp4
https://support.google.com/analytics/answer/1745147?hl=en